[Conflicts between healthcare professionals and families of a multi-ethnic patient population in the intensive care unit].
Bibliographic record
Abstract
OBJECTIVE: To investigate which factors contribute to conflicts between healthcare professionals and family members from ethnic minority groups during medically critical situations in hospital. DESIGN: Descriptive, ethnographic research. METHOD: Ethnographic fieldwork was carried out in one intensive care unit (ICU) of a multi-ethnic urban hospital in Belgium in the period January-June 2014. Data were collected by means of negotiated interactive observation, in-depth interviews with healthcare professionals and examining the patients' medical files. Data were analysed using grounded theory procedures. RESULTS: Conflicts were primarily related to the participants' different views on 'good care'. Healthcare providers' (HCPs') views on good care were primarily grounded on a biomedical care model, whereas families' views on good care were mainly inspired by a holistic care approach. According to HCPs, giving good care included fighting the disease efficiently with great scientific competence, but family members considered this rather as attending to the patient and giving bedside care, amongst other things. The HCPs' biomedical vision on good care was strengthened by the strict application of ward regulations, characterizing the ICU setting. The families' holistic views on good care were strengthened by specific ethno-familial characteristics, including their ethno-cultural background. However, ethno-cultural differences only contributed to conflict if the policy context on the ICU could provoke this conflict. CONCLUSION: Conflicts cannot be exclusively linked to ethno-cultural differences. Structural, functional characteristics of the ICU contribute substantially to conflict development. Effective conflict prevention should, therefore, not only focus on ethno-cultural differences but should also focus sufficiently on the structural context and ward policy.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".