Exploring how IBCLCs manage ethical dilemmas: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Professional health care practice should be based on ethical decisions and actions. When there are competing ethical standards or principles, one must choose between two or more competing options. This study explores ethical dilemmas experienced by International Board Certified Lactation Consultants. METHODS: The investigator interviewed seven International Board Certified Lactation Consultants and analyzed the interviews using qualitative research methods. RESULTS: "Staying Mother-Centred" emerged as the overall theme. It encompassed six categories that emerged as steps in managing ethical dilemmas: 1) recognizing the dilemma; 2) identifying context; 3) determining choices; 4) strategies used; 5) results and choices the mother made; and 6) follow-up. The category, "Strategies used", was further analyzed and six sub-themes emerged: building trust; diffusing situations; empowering mothers; finding balance; providing information; and setting priorities. CONCLUSIONS: This study provides a framework for understanding how International Board Certified Lactation Consultants manage ethical dilemmas. Although the details of their stories changed, the essence of the experience remained quite constant with the participants making choices and acting to support the mothers. The framework could be the used for further research or to develop tools to support IBCLCs as they manage ethical dilemmas and to strengthen the profession with a firm ethics foundation.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".