Delivering Team-Based Primary Care for the Management of Chronic Low Back Pain: An Interpretive Description Qualitative Study of Healthcare Provider Perspectives
Bibliographic record
Abstract
Background: Chronic low back pain (LBP) is a prevalent and disabling health issue. Team-based models of primary care are ideally positioned to provide comprehensive care for patients with chronic LBP. A better understanding of primary care team perspectives can inform future efforts to improve how team-based care is provided for patients with chronic LBP in this practice setting. Aims: The aim of this study was to understand health care providers' experiences, perceived barriers and facilitators, and recommendations when providing team-based primary care for the management of chronic LBP. Methods: We conducted an interpretive description qualitative study based on focus group discussions with health care providers from team-based primary care settings in Ontario, Canada. Data were analyzed using thematic analysis. Results: We conducted five focus groups with five different primary care teams, including a total of 31 health care providers. We constructed four themes (each with subthemes) related to experiences, perceived barriers and facilitators, and recommendations to providing team-based primary care for the management of chronic LBP, including (1) care pathways and models of service delivery, (2) team processes and organization, (3) team culture and environment, and (4) patient needs and readiness. Conclusions: Primary care teams are implementing diverse care pathways and models of service delivery for the management of patients with chronic LBP, which can be influenced by patient, team, and organizational factors. Results have potential implications for future research and practice innovations to improve how team-based primary care is delivered for patients with chronic LBP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".