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Record W2073141549 · doi:10.1177/1098214007312630

Using Self-Assessments to Detect Workshop Success

2008· article· en· W2073141549 on OpenAlex
Marcel D’Eon, Leslie Sadownik, Alexandra Harrison, Jill Nation

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Evaluation · 2008
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsAlberta Health ServicesUniversity of British ColumbiaUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyReliability (semiconductor)Applied psychologyProgram evaluationScale (ratio)Gold standard (test)Medical educationMedicineStatistics

Abstract

fetched live from OpenAlex

An accepted gold standard for measuring change in participant behavior is third-party observation. This method is highly resource intensive, and many small-scale evaluations may not be in a position to use this approach. This study was designed to assess the validity and reliably of aggregated group self-assessments as one way to measure workshop effectiveness. In this study, participants completed a pre-, post-, and retrospective self-assessment on their perceived skill level in delivering feedback. Trained raters scored recorded role-play episodes. A statistically and practically significant difference in feedback skills was detected in both the self-assessments and observer ratings. The instruments used to assess participants' feedback skills had acceptable reliability. Those charged with workshop evaluation should have some confidence that aggregated self-assessments can be used to help determine workshop effectiveness.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.208
GPT teacher head0.549
Teacher spread0.341 · 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