Can fitness and movement quality prevent back injury in elite task force police officers? A 5-year longitudinal study
Bibliographic record
Abstract
Elite police work has bursts of intense physically demanding work requiring high levels of fitness, or capacity, and movement competency; which are assumed to increase one's injury resilience. The purpose of this study was to follow members of an elite police force (N = 53) to test whether back injuries (N = 14) could be predicted from measures of fitness and movement quality. Measures of torso endurance, relative and absolute strength, hip ROM and movement quality using the Functional Movement Screen(TM) and other dynamic movement tests were obtained from every officer at baseline. When variables were grouped and considered holistically, rather than individually, back injury could be predicted. Seven variables best predicted those who would suffer a back injury (64% sensitivity and 95% specificity for an overall concordance of 87%). Overall, the ability to predict back injury was not high, suggesting that there is more complexity to this relationship than is explained with the variables tested here. Practitioner Summary: Members of elite police forces have exposure to intense physically demanding work. Increased levels of fitness and movement competency have been assumed to increase injury resilience. However, complexity in the interactions between exposure, movement competency, training, fitness and injury may occlude the true relationship between these variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".