The Inevitable Challenge of Ethical Dilemmas in Optometry, Part 1: When Confidentiality is Tested
Bibliographic record
Abstract
Healthcare professionals often face ethical dilemmas, which arise when two ethical principles conflict. Despite the potential for psychological consequences, no study has examined ethical dilemmas in the field of optometry. Objective. This article is the first in a series of three pertaining to a joint study that aimed to identify and describe the ethical dilemmas faced by optometrists. Method. An online survey sent to 1,393 optometrists asked them about various categories of ethical dilemmas. Unlimited space was provided for explanations. Results. Each of the 22 ethical dilemmas proposed had previously been encountered by between 3.75% and 67.9% of the 240 respondents. This first article reports that ethical dilemmas involving confidentiality are varied and those pertaining to the filling out of driver’s licence forms had previously affected 40% of the participants. Conclusion. Optometrists regularly face tough ethical decisions for which knowledge of the legislation and regulations alone is insufficient. The results will be revealed in the next two articles in this series, with the last one broaching the discussion of how to optimize the management of ethical issues in the field of optometry.
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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 | 0.103 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.050 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".