Women’s Issues in Transportation: Summary of the 4th International Conference, Volume 2: Technical Papers (2011)

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55 Traffic Violations Versus Driving Errors Implications for Older Female Drivers Sherrilene Classen and Orit Shechtman, University of Florida, Gainesville Yongsung Joo, Dongguk University, Seoul, South Korea Kezia D. Awadzi and Desiree Lanford, University of Florida, Gainesville Research has shown that rates for motor vehicle–related crashes are twice as high for older men as for older women, but the proportion of fatalities is higher for older women. To better understand driving errors made in crashes and to suggest prevention strategies, this study (a) classified viola- tions underlying crashes into errors made during on-road assessments; (b) quantified age, gender, and types of driving errors as predictors of postcrash injury; and (c) examined whether different violations and driving errors occur in dif- ferent age cohorts (£75 and >75 years). The 2005 Florida Traffic Crash Records Database (N  5,345 older drivers) was used to select violations underlying crashes. The mean age was 76.08 (standard deviation  7.10), with 2,445 (45.7%) female drivers. Female drivers had statistically significantly more failure to yield (intersection or alley– driveway), failure to obey required traffic controls, and speed-related violations predictive of crash-related injuries. A greater percentage of injured female drivers made statisti- cally significantly more yielding errors (p < .001) and more speed regulation and gap acceptance (p < .05) errors. These findings generally held true when younger (£75 years) and older (>75 years) women were compared with their age cohorts. The findings show that compared with older male drivers, older female drivers are at a greater risk for injuries from crash-related violations and driving errors. This find- ing holds true when younger and older female drivers are compared with their age cohorts. Injury prevention strate- gies on the person, vehicle, and environmental levels must receive serious consideration and be tested empirically for effectiveness. In 2006, the United States had approximately 30 mil-lion drivers ages 65 and older, (1); by the year 2030, the number of older drivers is expected to more than double, making up 25 percent of the total driving popu- lation (2). This future group of older drivers is predicted to have a larger proportion of women driving more often and for longer distances (3–5). While past research findings have been recognized as predominately “gender neutral,” researchers are calling for attention to differences between male and female older drivers in research, practice, and in the development of policies supporting transportation and mobility services (6). Existing studies illustrate significant gender differences in driving patterns (7), crash risk and protective factors (8, 9), crash types and rates (10, 11), crash injury or fatality outcomes (12), and self-regulation of driving and driving cessation (13). Specifically, and com- pared with older male drivers, older female drivers have limited driving exposure, drive fewer miles on freeways, are less confident about their driving skills (14), and display driving errors such as incorrect vehicle speed for environ- mental conditions (15). Women drove fewer miles, drove more on local roads versus highways, and reported greater avoidance of difficult driving situations (e.g., in rain or in high traffic) (16, 17). Trend data predict that older women will drive more often and for more years of their lives (5). Given the current gender characteristics discussed above and the knowledge that older women are more seriously injured in crashes, the purpose of this research was to exam- ine gender differences among injured drivers. The study had three aims: (a) to examine gender differences among injured drivers in committing driving violations, (b) to

56 WOMEN’S ISSUES IN TRANSPORTATION, VOLUME 2 examine gender differences among injured drivers when committing driving violations that were classified as driv- ing errors, and (c) to examine whether different viola- tions and errors occur for different age groups (£75 years and >75 years) within the older driving population. A driving violation is defined as a behavior that engages in deliberate breaching of safe driving practices (18, 19). Violations were operationalized as those citations adminis- tered by law enforcement officers in accordance with Flor- ida state statutes. These included criminal violations (e.g., driving under the influence), nonmoving violations (e.g., improper parking), and noncriminal moving violations (e.g., failure to yield right-of-way or exceeding the speed limit) (20). For the purpose of investigating the second of the study’s aims, the study was interested in driving viola- tions that could be classified as driving errors; therefore, it focused on noncriminal moving violations. Driving errors are indicated by a maneuver executed erroneously, such as a lane maintenance error or a speeding error (21). To classify the violations, the study used visual scanning errors in combination with seven driving errors operationalized in the National Older Driver Research and Training Center’s (NODRTC’s) on-road assessment (22). These errors included those made during yielding, speed regulation, gap acceptance, lane maintenance, signaling, vehicle position, and adjustment to stimulus or traffic signs and errors made during visual scanning (Table 1). RATIONALE AND SIGNIFICANCE A gap exists in gender differentiation by type of driving violation or type of driving error among older female drivers who sustained a crash-related injury. Bridging this gap will provide information on the female gender differences—as well as on age differences among females— pertaining to type of violation or type of driving error pre- dictive of crash-related injuries. In turn, this information will make it possible to recommend strategies for practice or policy changes and to suggest future research opportu- nities for gender-specific injury prevention. METHODS This study was approved by the University of Florida’s Institutional Review Board. Design A cross-sectional design was used for studying driving violations by analyzing the 2005 Florida Traffic Crash Records Database (FTCRD) (N  526,833). NODRTC operational definitions were used to study seven driving errors (Table 1) (22). Sample Subjects in the FTCRD were all crash-involved drivers. Subjects were included in the study if they were >65 years of age, had daytime crashes, were driving automobile or automobile derivatives (not scooters, bicycles, golf carts or heavy-duty vehicles), and did not have noncriminal moving violations. Subjects were excluded if they had missing data or criminal moving violations (e.g., drunk driving) or if their age, gender, or injury status was unknown. Procedure When applied to the 2005 FTCRD driver population (N  526,833), the inclusion–exclusion criteria listed above yielded a final sample of 5,345 older drivers.1 Figure 1 displays the process of participant inclusion and exclusion. Driving violations recorded in the 2005 FTCRD were matched to driving errors by identifying 32 driving violations that were collapsed to 16 non- criminal moving violations. These were then presented electronically to three experts knowledgeable of driv- ing violations and driving errors. These experts were a senior-most commander in the Florida Highway Patrol with 15 years of law enforcement experience and two occupational therapists: one certified driving rehabilita- tion specialist with 6 years’ experience in this field and one occupational therapist trained in driving evaluation with 2 years’ experience in this area. Operational definitions for each driving error are pre- sented in Table 1. The expert raters were asked to choose up to two out of the seven driving errors (vehicle posi- tioning, lane maintenance, speed regulation, yielding, signaling, adjustment to stimuli and traffic signs, and gap acceptance) that match each violation. Examples of vio- lations are given in Table 2. The raters were instructed to select only one driving error for a given traffic vio- lation if they believed with great confidence that only that single error was related to the violation. Based on the raters’ responses, points were assigned to each error. When a rater chose two errors, 1 point was assigned to each error. When a rater chose only one error, 2 points were assigned to the error. To calculate the total scores, the points assigned by all three raters were summed for each driving error. For example, for the traffic violation 1 A procedure for database management of the 2005 FTCRD is avail- able from the first author of this study at sclassen@phhp.ufl.edu.

57TRAFFIC vIOLATIOnS vERSUS DRIvInG ERRORS of failure to yield for emergency vehicle, Rater 1 chose the driving errors yielding and adjustment to stimuli and traffic signs, but Rater 2 and Rater 3 chose only adjust- ment to stimuli and traffic signs as a corresponding driv- ing error. Thus, 5 points were assigned to adjustment to stimuli and traffic signs, 1 point to yielding, and 0 points to the other errors. Under this system of match- ing errors and violations, the violation of failure to yield for emergency vehicle had the strongest relation to the driving error adjustment to stimuli and traffic signs, as it TABLE 1 Operational Definitions of Driving Errors Driving Error Definition vehicle position Refers to the position of the vehicle (anterior or posterior) in relation to other vehicles and to objects and pavement (anterior or posterior) markings. This captures following distance during forward movement and vehicle spacing during lane changes and merges. Examples of errors: traveling too closely, inadequate space cushion during merge or lane change, and stopping across a crosswalk or too far back from either pavement markings or other vehicles. Lane maintenance Refers to the lateral (side-to-side) positioning of the vehicle during driving maneuvers (turns, straight driving, lane changes) and while stopped. Reflects ability to maintain steering control. Examples of errors: drifting out of driving lane, encroachments on perpendicular traffic or wide turns, and parking outside designated space markings. Speed regulation Reflects ability to follow and maintain speed regulation limits and to have adequate control of acceleration and braking features of the vehicle. Examples of errors: not coming to a complete stop at a stop sign, traveling too slow or too fast, inadequate merging speed regulation, and abrupt or inappropriate braking or acceleration. Yielding Giving right-of-way when appropriate. Refers to the ability to recognize common rules of road safety. Yielding is assessed at four-way or two-way stop intersections, right turns on red, and merges. Signaling proper use of turn signals. Examples of errors: leaving the turn signal on, not using the turn signal when turning, and using the turn signal inappropriately (wrong signal for a given turn, signaling too short until maneuver). Adjustment to stimuli Ability to respond appropriately to driving situations. This category captures the ability to adjust appropriately to and traffic signs changing road sign information, other vehicle movements, and pedestrian movements and to recognize potential hazards. Examples of errors: not adjusting speed regulation for posted limits, not following proper evaluator instructions, choosing improper lane from posted signage, and improper response to traffic or pedestrian movement. Gap acceptance Choosing an appropriately safe time and or spacing distance to cross in front of oncoming traffic (unprotected left turn). Errors in gap acceptance are based on evaluator judgment given the speed regulation of oncoming traffic and number of lanes to be crossed. Errors in gap acceptance consist of driver estimates that are both too short and too long for the given speed regulation and distance to be traveled. Merged events, drivers, vehicles, and violation files in SPSS 15.0 Removed drivers younger than 65 years and without age, gender, or injury data: N = 35,421 Selected drivers of automobiles, vans, or light trucks, or pickups: N = 33,537 Removed drivers who did not drive in daytime: N = 25,578 Removed drivers with no cited violations, drivers with criminal and nonmoving violations or careless driving: N = 6,836 Removed drivers with violations with low level of agreement among expert rates for matching violations to driving errors: Final N = 5,345 Drivers file N = 526,833 Vehicles file N = 526,833 Events file N = 268,605 Violation file N = 232,083 k FIGURE 1 Procedure for including or excluding drivers from the 2005 FTCRD.

58 WOmEn’S ISSUES In TRAnSpORTATIOn, vOLUmE 2 had the best rater matching and therefore the best score. All the other traffic violations were similarly matched to a driving error. After the first round of expert ratings and matching, the violation-to-error classifications with a low level of rater endorsement (score < 4) were selected. These violation- to-error classifications included failure to yield, failure to stop, right turn on red light, improper turn or U-turn, and improper backing up. The raters were asked to reclassify the violations in this set of classifications into one driving error type. The raters were in complete agreement on failure to yield but not on any of the other violations; therefore, failure to yield and all of the violation-to-error classifica- tions from the first round of reviews were used in the subse- quent analyses, but the other driving error categories were excluded. The final violation-to-error classification yielded six of the eight driving errors (no statistically significant agreement for signaling and visual scanning). In the FTCRD, crash-related injuries are coded as fatal (<1%), incapacitating (6%), noncapacitating (16%), possible (22%), and none (56%). For the purpose of this study, the injury categories were collapsed to a dichoto- mous yes–no variable. Analyses The data were analyzed to answer the three aims of the study (SAS 9.0) with the univariate procedure for descriptive statistics. To examine the effects of gender and age within the female group in traffic violations and errors among injured drivers, a contingency table was used and Fisher’s exact test was conducted for each traf- fic violation. (The chi-square test was not used because some traffic violations in the contingency table had fewer than five observations.) ReSultS The mean age was 76.08 (standard deviation 5 7.10), with 2,445 drivers (45.7%) being female. Table 2 dis- plays driver demographics, traffic violations, driving errors, and injury. The highest number of violations occurred as yielding violations. Failure to obey required traffic control was the second-largest category, followed by speed-related violations. A subanalysis of drivers cited for speed-related violations indicated that 23 (5.9%) were traveling above the posted speed limit, 87 (22.0%) were moving at the posted speed limit, and 284 (72.1%) were estimated to be driving at speeds lower than the posted speed limit. The highest numbers of errors were made in yielding, gap acceptance, and speed regulation. Forty-five percent of the group had sustained crash- related injuries. Table 3 displays the numbers and percentages of injured drivers and p-values for traffic violations predict- ing injuries, by gender. For all violations (except viola- tion of a flashing light), the percentage of injured female drivers was greater than the percentage of injured male drivers. For example, 774 drivers (342 males and 432 females) committed a failure to yield violation (left turn). Of these 774, a total of 432 female drivers (58.22%) and 342 male drivers (41.35%) were injured. The results of Fisher’s exact test (p < .001) showed that the difference between the two percentages was significant. Failure to yield (intersection or alley or driveway), failure to obey required traffic controls, and speed-related violations also showed statistically significant differences between injured male and female drivers, with higher percentages of older female drivers sustaining injuries. more injured male drivers (71.43%) were cited for violation of a flash- ing light than were injured female drivers, but this differ- ence was not statistically significant. Table 4 displays the numbers, percentages, and p-values for injured drivers by gender and driving error. A greater percentage of injured female drivers made driving errors compared with injured male drivers across all six catego- ries of driving errors, but only three of the categories were statistically significant. These were yielding errors (p < .001) and errors for speed regulation and gap acceptance (p < .05). no statistically significant gender differences were indicated for vehicle positioning, lane maintenance, and adjustment to stimuli driving errors. TABLE 2 Driver Demographics, Traffic Violations, Driving Errors, and Injury in the 2005 FTCRD number variable (N = 5,354) percent Driver demographicsa Female 2,445 45.7 Traffic violations Failure to yield (left turn) 1,569 29.4 Failure to yield (intersection) 1,116 20.9 Failure to yield 1,032 19.3 Failure to yield (alley or driveway) 448 8.4 Failure to obey required traffic control 406 7.6 Speed-related violation 394 7.4 Following too closely 166 3.1 Failure to drive in single lane 96 1.8 pedestrian violation (e.g., failure to yield to pedestrian) 46 0.9 Failure to yield for emergency driver 18 0.3 violation of right-of-way 14 0.3 violation of flashing light 13 0.2 Driving errors vehicle positioning 166 3.1 Lane maintenance 96 1.8 Speed regulation 394 7.4 Yielding 4,179 78.2 Adjustment to stimuli 104 1.9 Gap acceptance 406 7.6 Injury (yes) 2,382 44.6 a mean age = 76.08 years (standard deviation = 7.10).

59TRAFFIC vIOLATIOnS vERSUS DRIvInG ERRORS Table 5 shows traffic violations predicting injuries by gender for the younger driver group (age # 75). Statisti- cally significant differences were identified for all failure to yield categories as well as failure to obey required traf- fic control devices (p 5 .002). Table 6 displays the driv- ing errors predicting injuries by gender for the younger group (age # 75). Statistically significant differences were identified for yielding (p < .001) and gap accep- tance (p 5 .002). Table 7 shows the traffic violations predicting inju- ries, by gender, for the older group (age > 75). Statisti- cally significant differences were identified for all failure to yield categories. Table 8 displays the driving errors predicting injuries, by gender, for the older group (age > 75). A statistically significant difference was identified for yielding (p < .001) only. diScuSSion of ReSultS The purpose of this research was to examine gender dif- ferences among older ($65 years) crash-involved drivers. The study had three aims: (a) to examine gender differ- ences among injured drivers who committed driving vio- lations, (b) to examine gender differences among injured drivers when committing driving violations that were classified as driving errors; and (c) to examine whether different violations and errors occur for different age groups (#75 years and >75 years) within the older driv- ing population. The increased risk of crash-related injury as people age is supported by the driving literature (4, 23–25). The findings of this study, however, demonstrate that older women emerge as a high-risk group for sustaining more serious crash-related injuries and that specific violations and driving errors are predictors of those crashes. Injured female drivers had statistically more failure to yield, failure to obey traffic control devices, and speed- ing (under the posted speed limits) violations. In terms of driving errors, female drivers made significantly more yielding, speed regulation, and gap acceptance errors as compared with male drivers. Younger women (65 to 75 years of age) are committing more violations that predict crash-related injuries (all failure to yield categories and failure to obey required traffic control devices) as com- pared with all drivers in that age group. Older women drivers (>75 years of age) have more failure to yield vio- lations (all categories) as compared with drivers in that age group. Similarly, compared with their age cohort, younger women (65 to 75 years of age) are committing more yielding and gap acceptance driving errors predic- tive of injuries, whereas older women (>75 years) are committing more yielding driving errors predictive of injuries when compared with their age cohort. TABLE 3 Traffic Violations Predicting Injuries, by Gender male Injured Female Injured p-value for Traffic violation number percent number percent Gender Difference Failure to yield (left turn) 342 41.35 432 58.22 <.001*** Failure to yield (intersection) 236 39.27 290 56.31 <.001*** Failure to yield 198 36.13 262 54.13 <.001*** Failure to yield (alley or driveway) 89 35.60 100 50.51 .002** Failure to obey required traffic control 87 40.28 101 53.16 .01* Speed related violation 70 28.69 59 39.33 .04* Following too closely 20 20.20 19 28.36 .26 Failure to drive in a single lane 19 39.58 28 58.33 .1 pedestrian violation 0 0.00 1 6.25 .35 Failure to yield for an emergency driver 3 27.27 3 42.86 .63 violation of right-of-way 2 40.00 6 66.67 .58 violation of a flashing light 5 71.43 3 50.00 .59 *p < .05, **p < .01, ***p < .001 level (two-tailed test). TABLE 4 Injured Drivers, by Gender and Driving Error male Injured Female Injured p-value for Driving Error number percent number percent Gender Difference vehicle positioning 20 20.20 19 28.36 .26 Lane maintenance 19 39.58 28 58.33 .10 Speed regulation 70 28.69 59 39.33 .04* Yielding 867 38.86 1,090 55.95 <.001*** Adjustment to stimuli 10 16.13 12 28.57 .15 Gap acceptance 87 40.28 101 53.16 .01* *p < .05, **p < .01, ***p < .001 level (two-tailed test).

60 WOmEn’S ISSUES In TRAnSpORTATIOn, vOLUmE 2 TABLE 5 Traffic Violations Predicting Injuries in Younger Group (Age # 75), by Gender male Injured Female Injured p-value for Traffic violation number percent number percent Gender Difference Failure to yield (left turn) 131 35.69 205 60.83 <.001*** Failure to yield (intersection) 105 36.33 151 55.93 <.001*** Failure to yield 86 32.21 120 53.81 <.001*** Failure to yield (alley or driveway) 41 33.06 48 48.98 .02* Failure to obey required traffic control 37 36.63 49 60.49 .002*** Speed related violation 33 25.38 30 37.04 .09 Following too closely 9 17.31 14 34.15 .09 Failure to drive in a single lane 8 38.10 13 54.17 .37 pedestrian violation 0 0.00 1 11.11 .32 Failure to yield for an emergency driver 1 25.00 2 33.33 1.00 violation of right-of-way 2 50.00 3 60.00 1.00 violation of a flashing light 5 83.33 0 0.00 .11 *p < .05, **p < .01, ***p < .001 level (two-tailed test). TABLE 6 Driving Errors Predicting Injuries in Younger Group (Age # 75), by Gender male Injured Female Injured p-value for Driving Error number percent number percent Gender Difference vehicle positioning 9 17.31 14 34.15 .09 Lane maintenance 8 38.10 13 54.17 .37 Speed regulation 33 25.38 30 37.04 .09 Yielding 365 34.73 527 56.48 <.001*** Adjustment to stimuli 8 20.51 7 29.17 .55 Gap acceptance 37 36.63 49 60.49 .002*** *p < .05, **p < .01, ***p < .001 level (two-tailed test). TABLE 7 Traffic Violations Predicting Injuries in Older Group (Age > 75), by Gender male Injured Female Injured p-value for Traffic violation number percent number percent Gender Difference Failure to yield (left turn) 211 45.87 227 56.05 .003* Failure to yield (intersection) 131 41.99 139 56.73 <.001*** Failure to yield 112 39.86 142 54.41 <.001*** Failure to yield (alley or driveway) 48 38.10 52 52.00 .04* Failure to obey required traffic control 50 43.48 52 47.71 .59 Speed related violation 37 32.46 29 42.03 .21 Following too closely 11 23.40 5 19.23 .77 Failure to drive in a single lane 11 40.74 15 62.50 .16 pedestrian violation nA nA nA nA nA Failure to yield for an emergency driver 2 28.57 1 100.00 .38 violation of right-of-way 0 0.00 3 75.00 .4 violation of a flashing light 0 0.00 3 75.00 .4 *p < .05, **p < .01, ***p < .001 level (two-tailed test). TABLE 8 Driving Errors Predicting Injuries in Older Group (Age > 75), by Gender male Injured Female Injured p-value for Driving Error number percent number percent Gender Difference vehicle positioning 11 23.40 5 19.23 .77 Lane maintenance 11 40.74 15 62.50 .16 Speed regulation 37 32.46 29 42.03 .21 Yielding 502 42.54 563 55.47 <.001*** Adjustment to stimuli 2 8.70 5 27.78 .21 Gap acceptance 50 43.48 52 47.71 .59 *p < .05, **p < .01, ***p < .001 level (two-tailed test).

61TRAFFIC VIOLATIONS VERSUS DRIVING ERRORS The implications are that planners of prevention pro- grams must understand and address activity demands embedded in these types of violations and driving errors. The demands related to failure to yield, failure to obey traffic control devices, yielding, speed regulation, and gap acceptance must be identified so as to understand and prevent crashes and related injuries. This study leads to a consideration of additional questions; for example, in considering client factors: • Do older female drivers have different visual and perceptual processing abilities as compared with older men (26)? • How can the effects of physical frailty that may have a greater impact on the severity of injuries sustained by older women be curtailed? • Should interventions primarily involve role com- petence strategies that provide practice opportunities for behind-the-wheel training and, in so doing, increase the driving skill of older women? • Compared with older women, why are younger women (£75 years of age) committing more violations and driving errors predictive of crash-related injuries? What are the best strategies to curtail such violations and driving errors? In considering vehicle factors, it is suggested that older women: • Consider vehicles with smart features (e.g., head restraints and front and side airbags) that can reduce the impact of crashes and therefore crash severity and risk of injury; • Be fitted to their vehicles for optimal use and con- trol of the features (e.g., pedal extenders for reaching pedals) or for safe positioning in terms of the features (e.g., tilt and telescoping steering wheels that can be adjusted to maintain a safe distance between the driver’s chest and the steering wheel); and • Use vehicles equipped with systems such as the OnStar in-vehicle system to alert authorities and receive help quickly after the occurrence of an adverse road event. In considering environmental factors, it is suggested that older women: • Pay attention to posted speed signs and traffic signs and observe the speed limit and traffic control devices; • Consider route–trip planning as an option to reduce yielding and gap acceptance maneuvers (e.g., plan a trip to include making protected left turns to reduce judgments on the distance and speed of oncoming traffic); and • Consider options such as alternative forms of trans- portation or help from volunteer drivers if a trip requires negotiating complex traffic situations. These suggested person, vehicle, and environmental recommendations point to future research, specifically, to studying the complexities embedded in the female gen- der, such as cultural expectations or role competencies related to the driving task. The effects of protective or alternative strategies in preventing crash-related injuries must also be studied among older female drivers. The types of errors made yield useful information for rehabilitation professionals and driving evaluators when reviewing the performance patterns of older adults, such as driving history or violation records (27). For example a violation of failure to obey a required traffic control device may be interpreted, based on the results of this study, as the potential for having difficulty with gap acceptance. Additional, more focused, testing of client factors (e.g., cognition), performance skills (e.g., visual perception), activity demands (e.g., sequencing and tim- ing), or contextual demands (e.g., roadway dynamics) underlying such a driving error may be necessary. In this way, rehabilitation professionals and driving evalua- tors may discern the underlying challenges for the older adult and choose adequate intervention strategies to best address such challenges. The limitations of this study pertain to using retrospec- tive data from which cause and effect or temporality can- not be inferred. Crashes may also be underrepresented. The violation-to-error classification may further over- or underrepresent the findings. The generalizability of the results may be limited by the inclusion of “noncriminal moving violations” only in a subset of older drivers, the examination of violations at one point in time, and the collapsing of injury into a dichotomous variable. Never- theless, to the authors’ knowledge, this is the first study that classifies violations as driving errors and that exam- ines younger and older age groups within the older driver population as a determinant of violations and driving errors that predict crash-related injuries. CONCLUSION The findings from the 2005 FTCRD have shown that, com- pared with older male drivers, older female drivers are at a greater risk for injuries from crash-related violations and driving errors. This finding holds true when younger and older female drivers are compared with their age cohorts. Injury prevention strategies on the person, vehicle, and environmental levels must receive serious consideration and be tested empirically for effectiveness. ACKNOWLEDGMENTS This research was supported by a grant from the Public Health–Health Professions Model Program Demonstra-

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