Ethical decision-making for community health care professionals with clients who are living at risk
Bibliographic record
Abstract
Nurses and other community health care professionals are often challenged by the ethical problems of clients who are living at risk, that is, clients who choose to make autonomous decisions related to personal situations that have the potential for negative outcomes. Although these encounters may cause some of the highest levels of stress in health care professionals, there is a noted lack of research in this area. This exploratory study, conducted using a constructivist qualitative methodology describes the experiences of nurses and other community health care professionals who are participating in ethical decision-making with clients who are living at risk. Constructivism was chosen in recognition that community health care professionals may describe their experiences with ethical decision-making in diverse ways and may experience a number of different realities of these experiences. Participants, purposively sampled via one-on-one interviews, described the intensity of complex client situations that they cope with. Four main themes emerged from their descriptions: "Our clients who are living at risk", "Worrying about our clients", "Finding a better way-how we cope", and "Frustrated by the system-hitting the brick wall". Powerful emotions including anxiety, frustration, anger, fear, guilt and helplessness- emotions that sound like moral distress- were part of the experiences. Concepts associated with the four themes included: personal and professional values and beliefs, client capability, use of legislation, resource allocation, ethical climates in organizations, client-directed care delivery, and collaborative practice within interdisciplinary teams. The findings of this study suggest significant implications for clinical practice, leadership, research, and education. Overall, there is a critical need to ensure that professionals have opportunities to deal with their emotions and concerns when coping with all ethical problems. Strategies that will assist this process include the establishment of supportive systems such as highly functioning interdisciplinary teams, reflective practice, and flexible transformational leadership approaches.
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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.009 |
| 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".