Accessing care in multidisciplinary pain treatment facilities continues to be a challenge in Canada
Bibliographic record
Abstract
BACKGROUND: Multidisciplinary pain treatment facilities (MPTFs) are considered the optimal settings for the management of chronic pain (CP). This study aimed (1) to determine the distribution of MPTFs across Canada, (2) to document time to access and types of services, and (3) to compare the results to those obtained in 2005-2006. METHODS: This cross-sectional study used the same MPTF definition as in 2005-2006-that is, a clinic staffed with professionals from a minimum of three different disciplines (including at least one medical specialty) and whose services were integrated within the facility. A comprehensive search strategy was used to identify existing MPTFs across Canada. Administrative leads at each MPTF were invited to complete an online questionnaire regarding their facilities. RESULTS: Questionnaires were completed by 104 MPTFs (response rate 79.4%). Few changes were observed in the distribution of MPTFs across Canada compared with 12 years ago. Most (91.3%) are concentrated in large urban cities. Prince Edward Island and the Territories still lack MPTFs. The number of pediatric-only MPTFs has nearly doubled but remains small (n=9). The median wait time for a first appointment in publicly funded MPTFs is about the same as 12 years ago (5.5 vs 6 months). Small but positive changes were also observed. CONCLUSION: Accessibility to public MPTFs continues to be limited in Canada, resulting in lengthy wait times for a first appointment. Community-based MPTFs and virtual care initiatives to distribute pain services into regional and remote communities are needed to provide patients with CP with optimal care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".