Accessing care within team-based models of primary care for the management of chronic low back pain in Ontario, Canada: a qualitative study of patient experiences
Bibliographic record
Abstract
PURPOSE: To understand experiences accessing care within team-based primary care models among adults with chronic low back pain (LBP). MATERIALS & METHODS: We conducted an interpretive description qualitative study and collected data using one-to-one semi-structured interviews. Participants were recruited from publicly funded, team-based primary care models in Ontario, Canada. RESULTS: We completed interviews with 16 adults with chronic LBP (9 women; median age of 66). Participants expressed a desire to access care from team-based models of primary care in hopes of alleviating pain and its impacts on daily life. Due to no direct out-of-pocket costs, co-location of healthcare providers, and the use of technology and virtual care, participants described an ease of accessing interprofessional care within team-based primary care models. Finally, participants described experiences with and expectations for timely access to care, being heard and understood by healthcare providers, and receiving coordinated care by an interprofessional team. CONCLUSIONS: Adults living with chronic LBP described overall positive experiences and specific expectations when accessing care within team-based models of primary care, whereby they experienced an ease of accessing interprofessional care with the hope of alleviating pain and its impacts. Results may be transferable to other chronic pain conditions and health system contexts.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".