Professionalism in healthcare: Time to renew commitment
Bibliographic record
Abstract
This paper re-examined professional behavior because of its prominent significance to healthcare safety and outcomes. The authors sought to better understand what factors contribute to what is seemingly an erosion of professional behavior within the modern healthcare environment. To date, publications focus on educational and institutional factors, applied locally to organizational and academic programs to teach, mentor professionalism and remediate misbehavior. The literature was studied to inform educators and practitioners alike of what may be unexplored drivers to witnessed unprofessional behaviors and to validate our current experience. First, a brief overview of the contemporary history and the classical tenets of professionalism were undertaken and then a three-pronged approach investigated potential influences that might affect professional behavior: (1) the effects of popular culture using social media as a proxy; (2) a review of academic education and training through the formal and informal curricula; and (3) the commodification of healthcare as a proxy for secular change. There were no discoveries that compared studies to evaluate the direct effects of popular culture and secular change on professional behaviors over time, since secular forces evolve, and societal variables don’t remain constant. However, findings indicated that while proper behavior declarations abound through professional organizations and academic curricula, professionalism wanes in the current health care environment. We assert that the external drivers within our respective secular societies be considered with more significant emphasis to weigh the root causes of unprofessional behavior to recognize and respond to these forces. Given the covenant to uphold professional values to promote patient safety and ethical dispositions, we call for the renewal of professionalism with the requisite industry, academic and secular insight to succeed.
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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".