Moral distress and patients who forego care due to cost
Bibliographic record
Abstract
BACKGROUND: In the US, many patients forgo recommended care due to cost. The ANA Code of Ethics requires nurses to give care based on need. Therefore, US nurses are compelled to practice in a context which breaches their professional ethical code. RESEARCH OBJECTIVES: This study sought to determine if nurses do care for patients who forgo treatment due to cost (PFTDC) and if so, does this result in an experience of moral distress (MD). RESEARCH DESIGN: Semi-structured interviews were transcribed and analyzed using a qualitative content analysis. PARTICIPANTS AND RESEARCH CONTEXT: A convenience sample of 20 nurses in practice for at least one year from a variety of health care setting participated. ETHICAL CONSIDERATIONS: This project was approved by the Michigan State University Biomedical Institutional Review Board. RESULTS: There were 19 female and one male nurse-participants, averaging 47 years old with an average of 10 years in practice. 18 reported caring for PFTDC. These 17 nurse-participants experienced a moderate degree of MD as a result, averaging 5.4 of 10 on the Moral Distress Thermometer. In the interviews, the following themes were identified, strategies to help PFTDC, and the broken US health care system which had the subthemes of preference for business over patient-oriented benefit, PFTDC using the emergency department, and limited support for treatment/management of PFTDC. CONCLUSIONS: The existence of this phenomenon places the profession of nursing in the US in a position of moral compromise and threatens to corrupt the institution of nursing in the US.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".