Moral Experience of Canadian Healthcare Professionals in Humanitarian Work
Bibliographic record
Abstract
INTRODUCTION: Expatriate healthcare professionals frequently participate in international relief operations that are initiated in response to disasters due to natural hazards or humanitarian emergencies in low resource settings. This practice environment is significantly different from the healthcare delivery environment in the home countries of expatriate healthcare professionals. Human rights, public health, medicine, and ethics intersect in distinct ways as healthcare professionals provide care and services in communities affected by crisis. PURPOSE: The purpose of this study was to explore the moral experience of Canadian healthcare professionals during humanitarian relief work. METHODS: This is a qualitative study with 18 semi-structured individual interviews based on Interpretive Description methodology. There are two groups of participants: (1) 15 healthcare professionals (nine doctors, five nurses, and one midwife) with more than three months experience in humanitarian work; and (2) three individuals who have experience as human resource or field coordination officers for humanitarian, non-governmental organizations. Participants were recruited by contacting non-governmental organizations, advertisement at the global health interest group of a national medical society, word of mouth, and a snowball sampling approach in which participants identified healthcare professionals with experience practicing in humanitarian settings who might be interested in the research. RESULTS: Five central themes were identified during the analysis: (1) examination of motivations and expectations; (2) the relational nature of humanitarian work; (3) attending to steep power imbalances; (4) acknowledging and confronting the limits of what is possible in a particular setting; and (5) recognition of how organizational forms and structures shape everyday moral experience. DISCUSSION: Humanitarian relief work is a morally complex activity. Healthcare professionals who participate in humanitarian relief activities, or who are contemplating embarking on a humanitarian project, will benefit from carefully considering the moral dimensions of this work. Humanitarian organizations should address the moral experiences of healthcare professionals in staff recruitment, as they implement training prior to departure, and in supporting healthcare professionals in the field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".