The Future of Health Care in New Brunswick: An Interview with Dr. Dennis Furlong
Bibliographic record
Abstract
Dr. Dennis Furlong is uniquely positioned to weigh in on current debates about the future of health care in New Brunswick given his decades-long career as a family physician, president of provincial medical societies, and government minister. In this interview, Furlong outlines his prescription for a sustainable health care system: a proposal to increase accountability among both providers and patients thereby reducing overuse (and misuse) of an overwhelmed and financially strained system to make it affordable and viable in the long term. Resume Dennis Furlong est la personne tout indiquee pour intervenir dans les debats actuels portant sur l’avenir des soins de sante au Nouveau-Brunswick etant donne sa carriere de plusieurs decennies en tant que medecin de famille, president des societes medicales provinciales et ministre du gouvernement. Dans la presente entrevue, M. Furlong expose les grandes lignes de ce que serait son ordonnance pour un systeme de soins de sante durable : accroitre la responsabilite des fournisseurs et des patients, ce qui reduirait la surutilisation (et l’utilisation abusive) d’un systeme sature et aux ressources financieres tres limitees afin de le rendre abordable et viable a long terme.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".