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Record W2581563352 · doi:10.12927/hcq.2017.25013

Implementation of Behavioural Supports Ontario (BSO): An Evaluation of Three Models of Care

2017· article· en· W2581563352 on OpenAlexaffabout
Michelle Grouchy, Nancy Cooper, Tommy Wong

Bibliographic record

VenueHealthcare Quarterly · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsOntario Long Term Care Association
Fundersnot available
KeywordsBest practiceHealth administrationHealth carePsychologyNursingProcess managementMedicineBusinessPublic relationsEnvironmental planningPolitical sciencePublic healthEnvironmental scienceManagementEconomics

Abstract

fetched live from OpenAlex

Behavioural Supports Ontario (BSO) was launched to enhance the healthcare services for Ontario's seniors, their caregivers and families living and coping with responsive behaviours associated with dementia and other neurological conditions. The implementation of the program varied across and within the local health integration networks (LHINs). By 2015, there were three BSO models operating within the long-term care (LTC) home sector: in-home BSO teams, a mobile team that serves multiple LTC homes within a sub-area of a LHIN and a LHIN-wide mobile team that provides services to all homes. A survey was undertaken to identify the differences among the BSO models of care in relation to care planning, collaboration and team building and home-level resident outcomes. We found that three years after implementation, LTC staff reported that the in-home BSO model out-performs the mobile team across all key measures. There is a role for mobile teams to provide expertise and sharing of best practices across the regions, but future policy and funding should focus on supporting the development of in-home BSO teams.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.472
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.270
GPT teacher head0.473
Teacher spread0.203 · 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.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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