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Record W3028080435 · doi:10.12927/hcq.2016.24482

The South West Local Health Integration Network Behavioural Supports Ontario Experience

2016· article· en· W3028080435 on OpenAlexafffundabout
Iris Gutmanis, Jennifer Speziale, Lisa Van Bussel, Julie Girard, Loretta M. Hillier, K. M. Simpson

Bibliographic record

VenueHealthcare Quarterly · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSt Joseph's Health CareLawson Health Research Institute
FundersOntario Ministry of Health and Long-Term Care
KeywordsPerceptionAddictionNursingHealth careMental healthMedicinePsychologyBusinessPsychiatryEconomic growth

Abstract

fetched live from OpenAlex

Creating a seamless system of care with improved system and patient outcomes is imperative to the estimated 35,000 older adults living with mental health problems and addictions in the South West Local Health Integration Network. Building on existing investments and those offered through the Behavioural Supports Ontario program, strategies to improve system coordination were put in place, cross-sectoral partnerships were fostered, interdisciplinary teams from across the care continuum were linked, and educational opportunities were promoted. This evolving, co-created system has resulted in a decrease in alternate level of care cases among those with behavioural specialized needs and improved client/family perceptions of care. Also, in fiscal year 2014/15, it provided more than 7,000 care providers with learning opportunities.

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)0.001
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0120.003
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.148
GPT teacher head0.401
Teacher spread0.252 · 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

Citations4
Published2016
Admission routes3
Has abstractyes

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