Stories of institutional and local policy change from Harvard’s Cambridge Health Alliance
Bibliographic record
Abstract
In May of 2017, myself and five other first and second year McGill Medical students embarked on a cultural exchange with Harvard medical students. This is an annual program run by Dr. Semaan, professor at Harvard Medical School, and McGill Medicine graduate. During the exchange, we had the opportunity to attend some pointed lectures which had the goal of illustrating some of the realities of the health care system in the Cambridge-Boston area. This article is a reflection on the talk given by Dr. David Bor of the Cambridge Health Alliance titled “Cambridge Health Alliance: A Public, Academic Community-Responsive Health Care System”, wherein he provided inspiring personal stories of institutional and policy change pursuits he was involved in in response to needs of the local population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.036 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 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".