Bibliographic record
Abstract
Purpose – The purpose of this article is to alert those in positions of trust and authority that there is an urgent need for improvement throughout the entire health profession credentialing process to fix defects at every stage, from employer responsibilities all the way up to licensing board responsibilities and government oversight. Design/methodology/approach – This paper takes the form of a narrative review. Findings – The assumption that layers of safeguards prevent dangerously incompetent or impaired practitioners from continuing to practice in American hospitals is, unfortunately, just that – an assumption. While the vast majority of healthcare professionals uphold the standards of their professions, a recent public health vulnerabilities report reveals serious defects throughout those safeguard layers and widespread harm that results from actions of relatively few determined miscreants who manage to evade them. This not only undermines public trust, but underscores ways in which governing boards, hospital executives and directors have found themselves liable for failings of their institution's quality assurance provisions. That vulnerability report is the result of investigation into one healthcare worker whose narcotic thefts and drug tampering resulted in thousands of patients exposed, dozens infected with hepatitis C, across several states and multiple missed opportunities to constrain. Originality/value – Findings of the Maryland state investigation, coupled with other documents, show that long-recognized ethical and legal responsibilities are not being met effectively.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".