Bibliographic record
Abstract
The modern health care system encounters new challenges, among which is fast-rising mental health problems. Evidence points to the increase in mental health conditions being driven not only by growing awareness, but also a substantial rise in the underlying prevalence. As a result, deft responses from the health care system are imperative. To address the growing burden from conditions that require early interventions and long-term management, such as mental health disorders, the system requires innovative solutions in health care delivery and provider payment. This thesis presents three chapters discussing various aspects of health care reforms, from the social change propelling rising mental health needs to the innovative team-based care delivery model, and concerns in the existing provider payment models, in particular relating to the physician gender pay gap. Chapter 1 investigates the extent to which social media are harmful for teenagers, leveraging rich administrative data from the Canadian province of British Columbia and quasi-experimental variation related to the introduction of wireless internet there. I find that high-speed wireless internet significantly increased teen girls’ severe mental health diagnoses – by 90% – relative to teen boys over the period when visual social media became dominant in teenage internet use. Chapter 2 assesses the efficacy of Ontario’s team-based care policy Family Health Teams. I find FHTs improved overall primary care quality, significantly reducing emergency room. I find evidence of an adjustment in mental health care provision: while early-wave FHTs substituted social workers for physicians, yielding no change in quality, later-wave FHTs saw significant quality improvements, with physicians and social workers collaborating. Chapter 3 explores the roles of selection and practice style in driving the well documented physician gender pay gap. I leverage rich administrative health care data from Ontario along with a quasi-experiment that randomly assigns physicians in emergency departments based on exogenous physician availabilities. I find that selection of female physicians to lower-paying shifts leads to a 5% gap in male and female physicians’ per-visit pay. Female physicians also spend around 10% more time on each visit, resulting in lower hourly wages.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".