Assessing the impacts of the Quebec primary care enrolment policies on patient-physician affiliation
Bibliographic record
Abstract
Objective: To introduce Random Forest (RF), a machine learning method, in an accessible way for health services researchers and highlight its unique considerations when applied to health administrative data. Data sources (or Study Setting):Physician claims' data from the universal public insurer linked with the Canadian Community Health Survey for the Canadian province of Quebec. Study Design:We describe in detail how RF can be useful in health services research, provide guidance on data set up, modeling decisions and demonstrate how to interpret results.We also highlight specific considerations for applying RF to health administrative data.In a working example, we compare RF with logistic regression, Ridge regression and LASSO in their ability to predict whether a person has a regular medical doctor. Data Extraction:We use survey responses to "do you have a regular medical doctor" from three cycles of the Canadian Community Health Survey (2007, 2009, 2011).Responses are linked with physician claims' data from 2002 to 2012.We limit our cohort to persons 40 years and older at the time of responding to the survey. Conclusions:We discuss the strengths and weaknesses of using RF in a health services research setting in comparison to using more conventional modeling techniques.Applying a RF model in a health services research setting can have advantages over conventional modeling approaches and we encourage health services researchers to add RF to their toolbox of predictive modeling methods.
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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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".