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Record W2586534219 · doi:10.1080/0142159x.2017.1286310

Twelve tips for planning and conducting a participatory evaluation

2017· article· en· W2586534219 on OpenAlexaff
Katherine Moreau

Bibliographic record

VenueMedical Teacher · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsParticipatory evaluationParticipatory GISCitizen journalismGeneral partnershipStakeholderParticipatory planningStakeholder engagementParticipatory action researchSociologyProcess managementMedical educationKnowledge managementManagement sciencePublic relationsComputer scienceMedicinePolitical scienceBusinessEnvironmental planningEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

Participatory evaluation involves a partnership between program evaluators and stakeholders. This paper provides tips for planning and conducting a participatory evaluation of a medical education program. The tips highlight the need to recognize the importance of judgment in participatory evaluation, assess the appropriateness of participatory evaluation for the setting, determine a predominant stream of participatory evaluation, and select stakeholders for participation carefully. The tips also suggest that one should initiate participation at the program planning stage, engage a participatory evaluator, develop an evaluation framework, associate participatory evaluation with more than just qualitative methods, and use technology to facilitate participation. Furthermore, the tips illuminate that while individuals can use participatory evaluation to build evaluation capacity, it is important that they use three dimensions (i.e. control of decision-making, stakeholder selection, depth of participation) for determining the level of "participatory-ness," as well as publish and reflect on their use of participatory evaluation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.853
GPT teacher head0.661
Teacher spread0.191 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations27
Published2017
Admission routes1
Has abstractyes

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