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Brief analysis of the best practice guideline by Registered Nurse's Association of Ontario—Facilitating Client Centred Learning (2012)

2018· article· en· W3031886168 on OpenAlexaboutno aff
Fen Zhou, Yufang Hao, Hong Guo, Lijiao Yan, Lulu Lyu, Xuejing Li, Jingya Ma

Bibliographic record

VenueZhonghua xiandai huli zazhi · 2018
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelineScope (computer science)Association (psychology)Quality (philosophy)PsychologyNursingService (business)Best practiceMedicineKnowledge managementMedical educationComputer scienceBusinessManagement

Abstract

fetched live from OpenAlex

Effective mastery of disease-related knowledge and skills plays a positive role in the good outcomes of patients. However, the complex hygiene and health environment and the specialized health information and service accessible to patients make it difficult for patients with different health awareness to master relevant knowledge or skills effectively. Registered Nurse's Association of Ontario (RNAO) proposed the best practice guideline-Facilitating Client Centred Learning based on the social construction theory and the best evidence available. This paper tries to analyze the guideline briefly from its development group, scope of application, subject content, quality evaluation, subsidiary information and preliminary application. Key words: Guidebooks; Facilitating Client Centred Learning; Brief analysis

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)0.026
metaresearch head score (Gemma)0.058
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: Review · Consensus signal: none
Teacher disagreement score0.250
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.008
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.083
GPT teacher head0.417
Teacher spread0.335 · 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
GenreReview

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

Citations0
Published2018
Admission routes1
Has abstractyes

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