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Record W4389131041 · doi:10.1057/s41599-023-02403-5

Evaluations of training programs to improve capacity in K*: a systematic scoping review of methods applied and outcomes assessed

2023· article· en· W4389131041 on OpenAlexaboutno aff
Samantha Shewchuk, James A. Wallace, Mia Seibold

Bibliographic record

VenueHumanities and Social Sciences Communications · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersBill and Melinda Gates Foundation
KeywordsInclusion (mineral)CategorizationSample (material)Medical educationCurriculumSample size determinationPsychologySystematic reviewComputer scienceMEDLINEKnowledge managementMedicinePolitical scienceArtificial intelligenceStatisticsPedagogy

Abstract

fetched live from OpenAlex

Abstract This paper examines how frequently K* training programs have been evaluated, synthesizes information on the methods and outcome indicators used, and identifies potential future approaches for evaluation. We conducted a systematic scoping review of publications evaluating K* training programs, including formal and informal training programs targeted toward knowledge brokers, researchers, policymakers, practitioners, and community members. Using broad inclusion criteria, eight electronic databases and Google Scholar were systematically searched using Boolean queries. After independent screening, scientometric and content analysis was conducted to map the literature and provide in-depth insights related to the methodological characteristics, outcomes assessed, and future evaluation approaches proposed by the authors of the included studies. The Kirkpatrick four-level training evaluation model was used to categorize training outcomes. Of the 824 unique resources identified, 47 were eligible for inclusion in the analysis. The number of published articles increased after 2014, with most conducted in the United States and Canada. Many training evaluations were designed to capture process and outcome variables. We found that surveys and interviews of trainees were the most used data collection techniques. Downstream organizational impacts that occurred because of the training were evaluated less frequently. Authors of the included studies cited limitations such as the use of simple evaluative designs, small cohorts/sample sizes, lack of long-term follow-up, and an absence of curriculum evaluation activities. This study found that many evaluations of K* training programs were weak, even though the number of training programs (and the evaluations thereof) have increased steadily since 2014. We found a limited number of studies on K* training outside of the field of health and few studies that assessed the long-term impacts of training. More evidence from well-designed K* training evaluations are needed and we encourage future evaluators and program staff to carefully consider their evaluation design and outcomes to pursue.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMeta-epidemiology (broad)
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.135
metaresearch head score (Gemma)0.347
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score0.714

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1350.347
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0290.029
Science and technology studies0.0020.003
Scholarly communication0.0080.007
Open science0.0030.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.942
GPT teacher head0.752
Teacher spread0.190 · 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

Labeled directly by 2 models reading the full record.

MetaresearchMeta-epidemiology (broad)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review
DomainMethods
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

Citations5
Published2023
Admission routes1
Has abstractyes

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