Who gets a family physician through centralized waiting lists?
Bibliographic record
Abstract
BACKGROUND: North American patients are experiencing difficulties in securing affiliations with family physicians. Centralized waiting lists are increasingly being used in Organisation for Economic Co-operation and Development countries to improve access. In 2011, the Canadian province of Quebec introduced new financial incentives for family physicians' enrolment of orphan patients through centralized waiting lists, the Guichet d'accès aux clientèles orphelines, with higher payments for vulnerable patients. This study analyzed whether any significant changes were observed in the numbers of patient enrolments with family physicians' after the introduction of the new financial incentives. Prior to then, financial incentives had been offered for enrolment of vulnerable patients only and there were no incentives for enrolling non-vulnerable patients. After 2011, financial incentives were also offered for enrolment of non-vulnerable patients, while those for enrolment of vulnerable patients were doubled. METHODS: A longitudinal quantitative analysis spanning a five-year period (2008-2013) was performed using administrative databases covering all patients enrolled with family physicians through centralized waiting lists in the province of Quebec (n = 494,697 patients). Mixed regression models for repeated-measures were used. RESULTS: The number of patients enrolled with a family physician through centralized waiting lists more than quadrupled after the changes in financial incentives. Most of this increase involved non-vulnerable patients. After the changes, 70% of patients enrolled with a family physician through centralized waiting lists were non-vulnerable patients, most of whom had been referred to the centralized waiting lists by the physician who enrolled them, without first being registered in those lists or having to wait because of their priority level. CONCLUSION: Centralized waiting lists linked to financial incentives increased the number of family physicians' patient enrolments. However, although vulnerable patients were supposed to be given precedence, physicians favoured enrolment of healthier patients over those with greater health needs and higher assessed priority. These results suggest that introducing financial incentives without appropriate regulations may lead to opportunistic use of the incentive system with unintended policy consequences.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".