Identifying Determinants of Performance for Females Completing a Paramedic Physical Employment Test
Bibliographic record
Abstract
Background: Sex disparities exist in employment and injury rates in the paramedic sector. Low success rates among females attempting physical employment standards could explain the elevated injury risk among female paramedics. Identifying factors that underpin successful work-related performance can inform pre-hire and return-to-work based physical training programs to address these disparities. \n \nPurpose: The purpose of this thesis was to identify the determinants of successful physical performance for females engaged in paramedic tasks. \n \nResearch Question 1: Participant demographics, college type, employment status and heart rate were obtained from female participants who completed the Ottawa Paramedic Physical Abilities Test (OPPAT), a physical employment standard for paramedics. These data were used in a logistic regression model to determine which factors could predict the likelihood of successfully completing the OPPAT. Females who were actively employed, who were educated in a public paramedic college, who had higher body mass, or those who had lower BMI were more likely to successfully complete the OPPAT. \n \nResearch Question 2: Lift duration and the time between peak knee and hip joint angular velocity during the Scoop and Barbell lift were compared between females who passed and failed the Ottawa Paramedic Physical Abilities Test. Four ANCOVAs were used for these comparisons where college type (public or private) and employment status (employed or unemployed) were used as categorical factors and body mass and BMI were used as covariates. No significant differences were found between passing and failing females. \n \nDiscussion: Modulating demographic factors that increase the likelihood of success could lead to improved performance outcomes, but other determinants should be explored to improve the predictive ability of the current model. Future research should continue to leverage emerging technology, such as markerless motion capture and unsupervised machine learning, to identify determinants of success for females in paramedic tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".