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Record W4380237268 · doi:10.1515/9780773571181

Emotional and Interpersonal Dimensions of Health Services

2003· book· en· W4380237268 on OpenAlexaboutno aff
Laurette Dubé, Guylaine Ferland, Moskowitz

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2003
Typebook
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsnot available
Fundersnot available
KeywordsInterpersonal communicationPsychologyEmotional healthSocial psychologyApplied psychologyMental healthPsychiatry

Abstract

fetched live from OpenAlex

Contributors examine the degree to which the provision of health care is influenced by characteristics of the health service organization, such as the administrative structure and the human resources available. They demonstrate that job satisfaction and conditions play an important role in shaping the quality and effectiveness of care and discuss the emotional support health care providers need to avoid long-term exhaustion and ensure well being. The contributors identify qualities of the client-provider interaction that lead to positive health care outcomes, such as providing information, responding to patient concerns, facilitating interactions with the health care system, and encouraging participation in personal health care and offer examples of innovative conceptual and analytical approaches to better health care practices. Contributors include Heather Boon (University of Toronto), Laurette Dubé, Carole A. Estabrooks (University of Alberta), Guylaine Ferland, Arlie Russell Hochschild (University of California, Berkley), Diane M. Irvine Doran (University of Toronto), Terrence Montague (Merck Frosst Canada), D.S. Moskowitz, Richard W.J. Neufeld (University of Western Ontario), Gilbert Pinard (McGill University), Debra L. Roter (John Hopkins Blooomberg School of Public Health), Dana Gelb Safran (New England Medical Center), and Krista K. Trobst (York University).

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.980
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2003
Admission routes1
Has abstractyes

Explore more

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