An examination of referrals declined for chronic pain care: There is increasing mental health complexity within care-seeking patients with chronic pain over time
Bibliographic record
Abstract
Background: Chronic pain is a complex disease that requires interprofessional care for effective management. Despite the need for multidisciplinary care, disease and health care inequities can prevent individuals from attaining adequate treatment. Factors such as mental health, cost, and distance to a health care center can contribute to health care accessibility inequality. The aim of this study is to examine declined referrals at the Toronto Academic Pain Medicine Institute (TAPMI) to determine the reason for declining care and number of declined referrals. Methods: A retrospective chart review of all declined referrals at TAPMI in 2018 and 2022 was conducted. Referral documentation and the intake decision were extracted from the electronic medical charts by the research team and verified by the clinical intake team. Chi-square tests were conducted to determine whether the proportion of declined referrals changed between the years reviewed. Results: < 0.00001). Other common reasons for declined referrals in 2018 and 2022 included duplicate service, no primary care provider, and health care service changes. Conclusion: Mental health complexities continue to be a significant barrier to health care service acquisition for individuals living with chronic pain. The increase in patient complexity from 2018 to 2022 highlights the need for integrated health care resources.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".