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Integrating Evidence-Based Interventions into Client Care Plans

2009· article· en· W9202926 on OpenAlexaff
Diane Doran, Paula Goering, Lynn Nagle, André Kushniruk, Irmajean Bajnok, Rani Srivastava

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionComputer scienceNursingMedicine

Abstract

fetched live from OpenAlex

Within the mental health care system, there is an opportunity to improve patient safety and the overall quality of care by integrating clinical practice guidelines with the care planning process through the use of information technology. Electronic assessment tools such as the Resident Assessment Inventory - Mental Health (RAI-MH) are widely used to identify the health care needs and outcomes of clients. In this knowledge translation initiative, an electronic care planning tool was enhanced to include evidence-based clinical interventions from schizophrenia guidelines. This paper describes the development of a mental health decision support prototype, a field test by clinicians, and user experiences with the application.

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)0.035
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.078
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.184
GPT teacher head0.544
Teacher spread0.361 · 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 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

Citations3
Published2009
Admission routes1
Has abstractyes

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