Development of a primary care low back pain pathway
Bibliographic record
Abstract
Background: Due to the sexual misconduct crisis and the COVID-19 pandemic, the Canadian Armed Forces (CAF) faces a historic shortage of personnel, including healthcare professionals. A key focus during reconstitution is the retention of experienced personnel. Medical attrition accounts for a third of workforce departures within the organization; therefore, improving the medical management of personnel would contribute to improved retention. Low back pain (LBP) is CAF members' third leading cause of medical attrition. The leadership of a primary care clinic in southwestern Ontario identified the development of a care pathway as a potential opportunity to improve the local management of LBP patients in a resource-conservative manner. Purpose: To develop an evidence-based care pathway and improve LBP management of local CAF personnel, ultimately decreasing patient disability and increasing the number of personnel who can maintain operational fitness and meet medical employment standards. Methods: After conducting a literature review to determine the quality of evidence for LBP care plans, I consulted with representatives from each healthcare profession within the clinic and completed an environmental scan of grey literature. In addition, I engaged in ongoing discussions with the local medical director, enabling resource customization and refinement. Results: Findings from the literature review indicated that LBP care pathways positively impact indicators such as patient disability, pain, and health-related quality of life. This information supported the selection of the STarT Back stratified care screening tool as the appropriate foundation for the LBP care pathway to be used in the clinic. Prominent themes from the consultations and environmental scan included therapist dependence, resource abundance, the potential for safety net abuse, and realistic opportunities for practice improvement. Consultations also indicated that earlier versions of the original proposed care pathway were overly complex. These processes \ncumulated into developing the Primary Care Low Back Pain Pathway. Conclusion: The Primary Care Low Back Pain Pathway is a resource that guides stratified care through categorizing risk for developing chronic back pain using the STarT Back screening tool. Based on the risk categorization, locally available resources are recommended.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".