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Record W2069993786 · doi:10.1353/his.2011.0020

Deinstitutionalization Reconsidered: Geographic and Demographic Changes in Mental Health Care in British Columbia and Alberta, 1950-1980

2011· article· fr· W2069993786 on OpenAlexvenueaboutno aff
Geertje Boschma

Bibliographic record

VenueHistoire sociale · 2011
Typearticle
Languagefr
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsInstitutionalisationDemographicsMental healthPoliticsMental health carePeriod (music)Mental hospitalLong-term careCriticismHealth careDemographyPolitical scienceGerontologyGeographyMedicineSociologyNursingPsychiatryLaw

Abstract

fetched live from OpenAlex

Cet article analyse le processus social connu sous le terme de désinstitutionnalisation, entre 1950 et 1980 en Alberta et en Colombie-Britannique, à partir de données démographiques sur les admissions et les sorties des hôpitaux psychiatriques. Une étude de la situation dans les deux provinces les plus à l'ouest du Canada permet d'analyser ces changements dans ces contextes régionaux. À la suite de nouveaux mécanismes de financement, d'un recadrage des soins communautaires et d'une critique croissante de la nature prétendument écrasante des grands établissements, chacun des trois principaux hôpitaux a réduit son envergure au cours des années 1950. Néanmoins, cette tendance n'a pas entraîné, au cours de cette période, une diminution du nombre global des patients hospitalisés. De fait, le nombre total des hospitalisations, et plus particulièrement celles de courte durée, a augmenté au moment où se produisait un phénomène de trans-institutionnalisation. Cette étude de cas reflète les principales tendances des soins apportés aux maladies psychiatriques de l'après-guerre et illustre les défis - sur un plan social, politique et culturel - auxquels s'est heurtée la reconstruction des soins en établissements.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
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
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.272
Teacher spread0.251 · 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 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

Citations6
Published2011
Admission routes2
Has abstractyes

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