Bibliographic record
Abstract
For chronic pain, 1 hub-and-spoke model and 4 stepped care models for the delivery of care in Canada and internationally were identified and described. No information was found on the use of the Oncology Care Model for chronic pain. For other medical conditions, 9 stepped care models, 5 hub-and-spoke models, and the Oncology Care Model for the delivery of care in Canada and internationally were identified and described. Patient-related outcomes used to evaluate the effectiveness of models of care for chronic pain include pain measures (e.g., intensity, duration), psychosocial outcomes (e.g., anxiety, depression), functional outcomes (e.g., disability, employment status), and health care utilization (e.g., opioid prescriptions, health care visits). Various barriers and facilitators to providing care for patients with chronic pain were identified in the consultations and the literature. The most common factors that influenced the care provided to patients with chronic pain pertained to funding, support, and collaboration from the government and locally; having a centralized intake and referral system; and leveraging existing resources. There appears to be considerable variation in the models of care used to address the care needs of patients with chronic pain. In Canada, there are provincial, regional, and local models, and local programs; some regions do not have a formalized approach for the provision of care for chronic pain 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 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".