Negative health impacts of navigating the healthcare system for musculoskeletal conditions: A scoping review protocol
Bibliographic record
Abstract
Musculoskeletal (MSK) conditions, particularly shoulders, knees, and the low back issues, place a significant burden on individuals, society, and healthcare systems. There is a lack of attention to negative health effects impacting patients because of their interactions to access appropriate diagnostics, assessments, and treatments. This scoping review intends to search and synthesize peer-reviewed evidence on the negative health impacts associated with navigating the healthcare system for MSK care. A scoping review will be conducted following the PRISMA guidelines for Scoping Reviews and Arksey and O'Malley's 5-step process. Six databases will be searched with no time or geographic limits. Included articles must meet all the following criteria: 1) the patients must be adults, 2) patients must be seeking care for their knee, low-back, or shoulder condition, 3) interacted with the healthcare system, and 4) experienced health impacts due to navigating the healthcare system. Information from each article will be charted in a pre-determined extraction. This protocol aims to share our methods ahead of analysis to increase rigour and transparency. The scoping review results will better elucidate the health impacts of the inaccessibility of high-quality care for MSK conditions. The findings also aim to inform the development of patient-centered outcomes to evaluate alterations to the current MSK pathways.
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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 | 0.123 | 0.109 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.017 |
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".