Can Physician Incentives Improve Continuity of Care For Patients Receiving Depression Treatment in the Primary Care Setting?
Bibliographic record
Abstract
Introduction In 2008, the province of British Columbia, Canada introduced financial incentives to encourage general practitioners (GPs) to assume the role of major source of care for patients seeking mental health treatment in primary care. If successful, this intervention could strengthen GP–patient attachment and consequently improve continuity of care. The impact of this intervention, however, has never been investigated. Aim To estimate the population level impact of physician incentives on continuity of care (COC). Method This retrospective study examined linked health administrative data from physician claims, hospital separations, vital statistics, and insurance plan registries. Monthly cohorts of individuals with depression were identified and their GP visits tracked for 12 months, following receipt of initial diagnosis. COC indices were created, one for any visits (AV) and another for mental health visits (MHV) only. COC (range: 0–100) was calculated using published formula that accounts for the number of visits and number of GPs visited. Interrupted time series analysis was used to estimate the changes in COC before (01/2005–12/2007) and after (01/2008–12/2012) the introduction of physician incentives. Results The monthly number of people diagnosed with depression ranged from 7497 to 10,575; yearly rates remained stable throughout the study period. At the start of the study period, mean COC for AV and MHV were 75.6 and 82.2 respectively, with slopes of –0.11 and –0.06. Post-intervention, the downward trend was disrupted but did not reverse. Conclusions Physician incentives failed to enhance COC. However, results suggest that COC could have been worse without the incentives. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".