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Record W2090869139 · doi:10.1080/01459350903505561

One Small Step at a Time: Implementing Continuous Quality Improvement in Child and Youth Mental Health Services

2010· article· en· W2090869139 on OpenAlexaff
Nicole Chovil

Bibliographic record

VenueChild & Youth Services · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsVancouver Native Health Society
Fundersnot available
KeywordsMental healthPatienceQuality managementNursingTotal quality managementQuality (philosophy)PsychologyService (business)Medical educationBusinessProcess managementMedicineMarketingPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Continuous quality improvement (CQI) is increasingly being adopted by health care, including child and youth mental health services. As part of the commitment to ongoing quality improvement, child and youth mental health teams in the Fraser region in British Columbia undertook CQI projects over a one year period (2007–2008). The projects covered a range of domains and service features and staff gained experience in developing projects. Patience, management support and ongoing dialogue were key elements in the implementation of CQI. Three projects are highlighted to illustrate how teams developed their own unique projects and the outcomes.

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)0.027
metaresearch head score (Gemma)0.031
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 score0.039
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0050.003
Open science0.0030.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.364
Teacher spread0.344 · 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

Citations15
Published2010
Admission routes1
Has abstractyes

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