Why do challenges still exist in primary care for patients with spinal cord injury?
Bibliographic record
Abstract
Despite having high healthcare needs, individuals with spinal cord injury (SCI) receive suboptimal primary care; they are less likely than able-bodied persons to receive preventive care and more likely to have unmet health care needs. The aim of this mixed quantitative (surveys) and qualitative (interviews) study was to gather primary care health provider and rehabilitation specialists’ perspectives on why these challenges persist despite the increasing body of evidence identifying delivery service gaps. Surveys were completed by 12 family physicians who referred individuals with SCI to an interprofessional primary care mobility clinic. Interviews were completed with eight SCI rehabilitation providers. Questions in both the survey and interviews were asked related to the barriers to the provision of optimal care for SCI, potential solutions, and preferred methods for knowledge dissemination. Skill and attitudinal reasons were offered for the lack of evidence to practice transfer including: the absence of patient self-management, poor access to specialists, lack of education for family practice physicians, fragmentation of community resources and co-ordination upon hospital discharge. Solutions offered included greater patient self-management, better access to specialists, specialized primary care services and provision of SCI guidelines and protocols. Participant explanations and solutions were then analyzed through a social disability lens to see if new understandings could be identified to explain the lack of uptake from research findings to clinical practice for this underserviced vulnerable population.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".