When boundaries blur - exploring healthcare providers' views of chiropractic interprofessional care and the Canadian Forces Health Services.
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal (MSK) conditions are primary reasons prohibiting Canadian Armed Forces (CAF) personnel from being deployed, with back pain the second most common activity-limiting condition. CAF provides a spectrum of services, including chiropractic care. There is a paucity of data related to chiropractic interprofessional care (IPC) within CAF healthcare settings. METHODS: A qualitative study, using an Interpretative Phenomenological Analysis (IPA) approach, involving 25 key informant interviews explored factors that impact chiropractic IPC. We used a systematic but not prescriptive process, based on a thematic analysis, to interconnect data to develop meaning and explanation. Initially, we explained and interpreted participant's experiences and meanings. Next, we used extant literature and theory, together with expert knowledge, to explain and interpret the meanings of participants' shared accounts. RESULTS: We present findings central to the domain, Role Clarity, as described in the IPC Competency Framework. Our findings call for strengthening IPC specific to MSK conditions in the CAF, including an examination of gatekeeping roles, responsibilities and outcomes. CONCLUSION: It is timely to investigate models of care that nurture and sustain inter-provider relationships in planning and coordinating evidence-based chiropractic care for MSK conditions, within the CAF, and its extended referral networks.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".