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Record W2972699497 · doi:10.12927/hcq.2016.24476

Divestment of Beds and Related Ambulatory Services to Other Communities While Maintaining a Patient- and Family-Centred Approach

2016· article· en· W2972699497 on OpenAlexaff
Deborah Corring, Deborah Gibson, Jill MustinPowell

Bibliographic record

VenueHealthcare Quarterly · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsCanadian Mental Health Association
Fundersnot available
KeywordsDivestmentAmbulatoryBusinessService (business)Mental illnessAmbulatory careMental healthService delivery frameworkNursingOrder (exchange)Public relationsMedicinePsychologyHealth carePsychiatryMarketingPolitical scienceEconomicsSurgeryEconomic growth

Abstract

fetched live from OpenAlex

Individuals living with serious mental illness who require acute and/or tertiary mental healthcare services represent one of the most complex patient groups in the healthcare service delivery system. Provincial mental health policy has been committed to providing services closer to home and in the community rather than an institution wherever possible for some time. This paper articulates the strategies used by one organization to ensure the successful transfer of beds and related ambulatory services to four separate communities. In addition a case study is also provided to describe in more detail the complex changes that took place in order to accomplish the divestments of beds and related ambulatory services to one of the partner hospitals.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0040.002
Open science0.0030.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.026
GPT teacher head0.291
Teacher spread0.265 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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