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Consumer input into standards revision: changing practice

2007· article· en· W2044866862 on OpenAlexaffabout
Georgiana Beal, Adrian Chan, Shelia Chapman, Jorge Edgar, Gloria McInnis-Perry, Margaret S. Osborne, Elaine Santa Mina

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2007
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsToronto Metropolitan UniversityUniversity of British ColumbiaSt. Paul's HospitalNova Scotia HospitalProfessional Engineers Ontario
Fundersnot available
KeywordsMEDLINEPsychologyMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

As part of ongoing quality improvement initiatives, the Canadian Standards for Psychiatric-Mental Health Nursing were recently revised. For the first time since the standards were published in 1995, the input of consumers of mental health services was sought. Thirty-one consumers from across Canada participated in focus groups, and answered questions related to the domains of practice as identified in the standards document. Through this input, consumers were able to inform the committee regarding areas of satisfaction and dissatisfaction from their unique perspective. Through this article, the process of consumer collaboration is illustrated in relation to how it shaped Standards revision, and finally how it affected the practitioners involved.

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)0.193
metaresearch head score (Gemma)0.292
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.193
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1930.292
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0150.018
Scholarly communication0.0170.011
Open science0.0040.015
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.490
Teacher spread0.419 · 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.

Study designNot applicable
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
Published2007
Admission routes2
Has abstractyes

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