Identification of the physician workforce providing palliative care in Ontario using administrative claims data
Bibliographic record
Abstract
BACKGROUND: Little is known about the physician workforce providing palliative care in Canada, and in Ontario specifically. We developed an algorithm to identify palliative care physicians using administrative claims data and validated it against a reference sample. We then applied the algorithm to all general practitioners/family physicians (GP/FPs) in the province of Ontario to describe and quantify those identified by the algorithm. METHODS: W e reviewed Ontario Health Insurance Plan claims from Jan. 1, 2008, to Dec. 31, 2011, to determine each physician's proportion of claims that were for palliative care. We empirically selected a data-driven cut-off, whereby physicians whose proportion of palliative care claims was above the threshold were defined as palliative care physicians. We validated the cut-off against a reference sample of physicians who self-identified as providing mostly palliative care in a study-specific survey. We then applied this algorithm to all GP/FPs in the province. RESULTS: We empirically selected 10% as the cut-off for the proportion of palliative care claims. This threshold had exceptional specificity and positive predictive value (97.8% and 90.5%, respectively) and adequate sensitivity (76.0%) when compared with the reference sample (n = 118). When applied to all GP/FPs in the province, the algorithm identified 276 practising mostly palliative care. Of these, 135 (48.9%) were women, 265 (96.0%) practised in urban locations, and 145 (52.5%) worked part time. INTERPRETATION: Our algorithm readily identified and quantified the workforce of palliative care physicians in Ontario. Such a tool has numerous applications for both health service planners and researchers.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".