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Program Evaluation in Pediatric Education

2002· article· en· W2014453428 on OpenAlexaffabout
Heather B. Porter, Shirley Avery, Linda Edmond, Rosalyn Straw, Jennifer Young

Bibliographic record

VenueJournal for Nurses in Staff Development · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsHospital for Sick ChildrenGolder Associates (Canada)
Fundersnot available
KeywordsAccountabilityParticipatory evaluationProgram evaluationNursingCitizen journalismSick childProcess (computing)Medical educationMedicineProcess managementPsychologyBusinessPolitical scienceComputer sciencePediatrics

Abstract

fetched live from OpenAlex

The goal of this project at the Hospital for Sick Children (HSC), Toronto, Canada, was to evaluate the effectiveness of structure, processes, and outcomes of three pediatric nursing education programs offered by HSC Nursing Education Services. Participatory evaluation was the process used for the project. Results of this project will assist nurse educators to show accountability for the effectiveness and efficiency of their programs, and provide a model for planning and evaluating future programs.

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)0.202
metaresearch head score (Gemma)0.273
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: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2020.273
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.253
GPT teacher head0.542
Teacher spread0.289 · 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

Citations7
Published2002
Admission routes2
Has abstractyes

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