There is a knowledge mobilization gap in musculoskeletal injury research in the military context
Bibliographic record
Abstract
LAY SUMMARY Injuries involving the musculoskeletal system are extremely common among military personnel and place a considerable burden on the individual in addition to impacting operational effectiveness. While a considerable amount of research is being conducted to reduce the prevalence and impact of such injuries, rates of members having their military career ended due to musculoskeletal injuries (MSKIs) remain high. Systems to identify, track and research MSKIs might be more successful if communication was improved between researchers and stakeholders by 1) engaging leadership at each step, 2) translating and disseminating research findings directly to support personnel (e.g., health care professionals, physical training staff), and 3) presenting and discussing research findings with service members to educated them and to have them assist in the interpretation of results. This perspective piece highlights a gap in knowledge sharing between the research community, military members, and practitioners supporting humans in uniform.
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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 | 0.083 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".