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Record W2157781276 · doi:10.12927/cjnl.2008.20290

The Influence of Teams, Supervisors and Organizations on Healthcare Practitioners' Abilities to Practise Ethically

2008· article· en· W2157781276 on OpenAlexaffvenue
Sarah Wall, Wendy Austin

Bibliographic record

VenueNursing leadership · 2008
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHealth carePsychologyMultidisciplinary approachHealth professionalsWork (physics)NursingEngineering ethicsMedical educationMedicineSociologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Healthcare practitioners make many important ethical decisions in their day-to-day practices. Questions arising in daily practice require practitioners to make prudent, balanced and good decisions, which are most effectively made interpersonally and reflectively. It is commonly assumed that the team-based structure of healthcare delivery can provide practitioners with the support needed to address ethical questions in their practice, especially if the team involves multidisciplinary collaboration. A phenomenological study was conducted in which the impact of the team and the larger organization on practitioners' experiences of dealing with moral challenges was uncovered. Various mental healthcare professionals shared their experiences of ethically challenging situations in their practices and described the ways in which their teammates and supervisors affected how they faced these troubling situations. These findings allow us to see that there is considerable room for healthcare managers, many of whom are nurses, to facilitate supportive, ethical environments for healthcare professionals. An understanding of the essential experience of practising ethically allows for an appreciation of the significance of the team's role in supporting it and enables healthcare managers to target support for ethical healthcare work.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.106
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.106
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.259
GPT teacher head0.462
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2008
Admission routes2
Has abstractyes

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