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Comparing Health Professional Work Orientation in French and Canadian Hospitals: Structural Influence of Patients in Open and Closed Units

2010· article· en· W2004524271 on OpenAlexafffundabout
Kristine Hirschkorn, Patricia Khokher, Ivan Sainsaulieu, Ivy Lynn Bourgeault

Bibliographic record

VenueComparative Sociology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsCentre for Addiction and Mental HealthHealth CanadaUniversity of Ottawa
FundersArts Research Board, McMaster UniversityCanada Research Chairs
KeywordsTypologyHealth professionalsBurnoutHeuristicIdeal (ethics)PsychologyWork (physics)Public relationsHealth careNursingSocial psychologySociologyPolitical scienceMedicineLawClinical psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract In this paper we present comparative qualitative data on the influence of patients on the relationship between health professionals and hospitals in France and Canada. We elaborate specifically on a typology that depicts the structural influence of patients in terms of open and closed communities. Our analysis reveals some key differences between the open and closed communities across the two countries. Health professionals in open communities in France were able to establish a stronger relationship with patients than those in Canada. Professionals in France described a weaker connection with their colleagues than in Canada, and this may be one of the triggers of stress, burnout and high turnover. There were more similarities among closed communities in Canada and France. Conceptualizing the structural influence of patients in terms of open and closed community ideal types is a useful heuristic device that moves the implicit and explicit influence of patients to the foreground of the analysis of the relations between health professions and organizations.

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.548

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0130.011
Scholarly communication0.0060.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.163
GPT teacher head0.511
Teacher spread0.348 · 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 designObservational
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

Citations4
Published2010
Admission routes3
Has abstractyes

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