Practicing the Public Good Exploring Ethical Issues in Public Health Practice
Bibliographic record
Abstract
Everyday ethics refers to the issues and decision-making practitioners are routinely faced with in their daily work. A quantitative, descriptive study examined everyday ethical issues in public health practice. The theoretical framework was based on Jameton's (1984) concepts of moral uncertainty, moral dilemma and moral distress in nursing. Moral distress may have negative consequences that ultimately lead to job dissatisfaction and leaving the profession. This phenomenon has been studied extensively in clinical practice, but comparatively little in public health practice. A questionnaire was administered to employees at the Thunder Bay District Health Unit (TBDHU) in Thunder Bay, Ontario, Canada. This instrument included an extensively-modified moral \ndistress scale (MDS) (Corley, Elswick, Gorman & Clor, 2001) supplemented by questions about demographics, ethics capacity and personal reflection. Altogether, moral problems were reported at low mean frequencies and intensities. Generally, front line providers and managers and supervisors experienced moral problems at higher levels than the sample average. Furthermore, education, membership in a professional association and job experience had a statistically-significant impact on moral problems. Finally, themes of recent moral or ethical dilemmas included: relationships; different interests/perspectives; fairness; knowledge sharing; and personal issues.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".