Priority interventions to improve the management of chronic non-cancer pain in primary care: a participatory research of the ACCORD program
Bibliographic record
Abstract
PURPOSE: There is evidence that the management of chronic non-cancer pain (CNCP) in primary care is far from being optimal. A 1-day workshop was held to explore the perceptions of key actors regarding the challenges and priority interventions to improve CNCP management in primary care. METHODS: Using the Chronic Care Model as a conceptual framework, physicians (n=6), pharmacists (n=6), nurses (n=6), physiotherapists (n=6), psychologists (n=6), pain specialists (n=6), patients (n=3), family members (n=3), decision makers and managers (n=4), and pain researchers (n=7) took part in seven focus groups and five nominal groups. RESULTS: Challenges identified in focus group discussions were related to five dimensions: knowledge gap, "work in silos", lack of awareness that CNCP represents an important clinical problem, difficulties in access to health professionals and services, and patient empowerment needs. Based on the nominal group discussions, the following priority interventions were identified: interdisciplinary continuing education, interdisciplinary treatment approach, regional expert leadership, creation and definition of care paths, and patient education programs. CONCLUSION: Barriers to optimal management of CNCP in primary care are numerous. Improving its management cannot be envisioned without considering multifaceted interventions targeting several dimensions of the Chronic Care Model and focusing on both clinicians and patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".