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Record W4394678371 · doi:10.1080/07481187.2024.2337189

<i>“More support, less distress?</i> ”: Examining the role of social norms in alleviating practitioners’ psychological distress in the context of assisted dying services

2024· article· en· W4394678371 on OpenAlexaff
Susilo Wibisono, Payam Mavandadi, Stuart Wilkinson, Catherine E. Amiot, Liz Forbat, Emma F. Thomas, Felicity Allen, Jean Decety, Kerrie Noonan, Kiara Minto, Lauren J. Breen, Madison Kho, Monique F. Crane, Morgana Lizzio‐Wilson, Pascal Molenberghs, Winnifred R. Louis

Bibliographic record

VenueDeath Studies · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversité du Québec à Montréal
FundersAustralian Research Council
KeywordsDistressContext (archaeology)Psychological distressMedicinePsychologyHealth professionalsClinical psychologyPsychiatryMental healthHealth care

Abstract

fetched live from OpenAlex

This study explores how providing assisted dying services affects the psychological distress of practitioners. It investigates the influence of professional norms that endorse such services within their field. Study 1 included veterinarians (N = 137, 75.2% female, Mage = 43.1 years, SDage = 12.7 years), and Study 2 health practitioner students (N = 386, 71.0% female, Mage = 21.0 years, SDage = 14.4 years). In both studies, participants indicated their degree of psychological distress following exposure to scenarios depicting assisted dying services that were relevant to their respective situations. In Study 1, we found that higher willingness to perform animal euthanasia was associated with lower distress, as were supportive norms. In Study 2, a negative association between a greater willingness to perform euthanasia and lower psychological distress occurred only when the provision of such services was supported by professional norms. In conclusion, psychological distress is buffered by supportive professional norms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.403
Teacher spread0.336 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

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