Understanding Team-Based Primary Care for the Management of Low Back Pain
Bibliographic record
Abstract
Low back pain (LBP) is a leading contributor to disability, high healthcare costs, and is one of the most common reasons for primary care visits. Calls to action emphasize a need to move away from biomedically focused and fragmented care models for LBP. Quality standards also recommend that patients with chronic LBP have access to interprofessional care. However, patients with LBP often receive fragmented care that is biomedically focused and access to interprofessional care remains a challenge. Team-based models of primary care may offer a solution. Team-based models of primary care include family physicians and/or nurse practitioners, as well as other healthcare providers, such as pharmacists, occupational therapists, physiotherapists, and social workers. Despite an expansion of team-based models of primary care across Canada and internationally, there are gaps in knowledge on the management of patients with LBP within this practice setting. The overarching aim of this dissertation was to advance knowledge on current and innovative approaches to team-based primary care for the management of patients with LBP. It includes the results of three inter-related interpretive description qualitative studies. The first study sought to understand experiences accessing care within team-based models of primary care among adults living with chronic LBP. The second study sought to understand healthcare providers’ experiences, perceived barriers and facilitators, and recommendations for providing team-based primary care for the management of chronic LBP. The third study sought to understand the perspectives of patients and primary care team members related to their experiences with an innovative team-based model of primary care where a physiotherapist was available as a first point-of-contact for patients with LBP. The results demonstrate that team-based models of primary care can contribute to positive patient and healthcare provider experiences and that these care models may improve patient access to integrated interprofessional care that is guideline adherent. However, opportunities remain to optimize care delivery in these contexts. The results have implications for future research and practice innovations aimed at improving the quality of care, experiences, and health outcomes of patients with LBP within team-based primary care settings.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".