“How Far Do You Go and Where Are the Issues Surrounding That?” Dilemmas at the Boundaries of Clinical Competency in Humanitarian Health Work
Bibliographic record
Abstract
Abstract You go from here to there, and here you're specialized in one particular sort of thing, there you may be asked to do all sorts of things outside your specialty. How far do you go and where are the issues surrounding that? Canadian physician discussing experiences in humanitarian aid work Health professionals working in humanitarian relief projects encounter a range of ethical challenges. Applying professional and ethical norms may be especially challenging in crisis settings where needs are elevated, resources scarce, and socio-political structures strained. Situations when clinicians must decide whether to provide care that is near the margins of their professional competency are a source of moral uncertainty that can give rise to moral distress. The authors suggest that responding ethically to these dilemmas requires more than familiarity with ethical codes of conduct and guidelines; it requires practical wisdom, that is, the ability to relate past experience and general guidance to a current situation in order to render a morally sound action. Two sets of questions are proposed to guide reflection and deliberation for clinicians who face competency dilemmas. The first is prospective and intended to aid clinicians in evaluating an unfolding situation. The second is retrospective and designed to support debriefing about past experiences and difficult situations. The aim of this analysis is to support clinicians in evaluating competency dilemmas and provide ethical care and services. Hunt MR , Schwartz L , Fraser V . “How Far Do You Go and Where Are the Issues Surrounding That?” Dilemmas at the Boundaries of Clinical Competency in Humanitarian Health Work . Prehosp Disaster Med . 2013 ; 28 ( 5 ): 1 - 7 .
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 | 0.035 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.043 | 0.075 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| 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".