Bibliographic record
Abstract
Headlines all over the world are issuing dire warnings about the state of healthcare in their respective countries. For example: in the UK, Why is Britain&s;s health service, a much-loved national treasure, falling apart? ( Edwards, 2023 ); in Canada, Health care is showing the cracks it&s;s had for decades. Why it will take more than cash to fix it ( Brend, 2022 ); in the US, Doctors aren&s;t burned out from overwork. We&s;re demoralized by our health system ( Reinhart, 2023 ); in Asia, Healthcare workers are at their breaking point but most don&s;t want to quit ( Leng, 2022 ); in Australia, Why Australia needs a systemic response to burnout ( Warby, 2022 ); and, in Europe, The health workforce crisis in Europe is no longer a looming threat – it is here and now ( WHO, 2023 ). Also strikingly similar is the almost universal acknowledgement that burnout among healthcare providers – which is a factor that diminishes care – has never been higher ( Berg, 2022 ; McEvoy & Thompson, 2022 ). There are as many hypotheses as there are headlines about the reasons for the healthcare crisis that many are experiencing. Researchers suggest, however, that the current deficit-based, hierarchal medical model, a model that conditions our clients/patients to be passive and compliant users of the system ( Haus, 2017 ; Rider et al., 2018 ), as well as the emphasis on economic and commercial metrics that are incongruent with personal and healthcare values ( Rider et al., 2018 ), has contributed to a dissonance that is negatively impacting healthcare around the world.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.041 |
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".