Choosing Wisely? Let’s Start with Working Wisely
Bibliographic record
Abstract
There is an increasing emphasis on quality and, relatedly, cost-effectiveness as it relates to the delivery of health care. Choosing Wisely is an initiative adopted by numerous specialties with the goal of starting a dialogue about efficient use of health care resources. People need to be able to access care to have an opportunity to choose wisely. There is a considerable amount of evidence that access to care is poor for specialty mental health care, particularly access to psychiatrists. Consequently, we suggest that psychiatrists and the broader mental health system need to consider working wisely, and in our paper outline key issues (for example, implementation of wait times and objective measures of need in a centralized referral management system; incorporation of performance indicators with longitudinal monitoring for continuous quality improvement) that need to be addressed to develop a mental health system that would allow people to access care to choose wisely.
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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".