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Record W2007898789 · doi:10.1111/acps.12368

Images of psychiatry and psychiatrists

2014· article· en· W2007898789 on OpenAlexaff
Heather Stuart, Norman Sartorius, Tiina Liinamaa

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsQueen's University
Fundersnot available
KeywordsSpecialtyPsychiatryPerceptionPsychologyMedicineFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This study surveyed medical teaching faculty to determine their attitudes toward psychiatry and psychiatrists. METHOD: We conducted a multisite survey of a probability sample of 1057 teaching medical faculty members from 15 academic teaching centers in the United Kingdom, Europe, and Asia stratified by early, middle, and late career stage. The average response rate across countries was 65%. RESULTS: The outstanding findings were that 90% of respondents considered that psychiatrists were not good role models for medical students, 84% thought psychiatric patients were unsuitable to be treated outside of specialized facilities, and 73% thought psychiatric patients were emotionally draining. We noted statistically significant differences by country, gender, career stage, and specialty. CONCLUSION: These results highlight why recruitment into psychiatry is problematic in many countries and suggest that greater attention should be given to improving the perception of psychiatrists as good role models and the efficacy of psychiatric treatments.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.385
Teacher spread0.365 · 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 source (direct Gemma or distilled Codex), 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

Citations88
Published2014
Admission routes1
Has abstractyes

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