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Record W2043031956 · doi:10.2190/h210-2n47-5x5t-21u4

Implementation Fidelity in a Teacher-Led Alcohol use Prevention Curriculum

2006· article· en· W2043031956 on OpenAlexaff
Melinda M. Pankratz, Julia Jackson‐Newsom, Steven M. Giles, Christopher L. Ringwalt, Kappie Bliss, Mary Lou Bell

Bibliographic record

VenueJournal of Drug Education · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsCurriculumFidelityMedical educationCurriculum developmentPsychologyMedicinePedagogyMathematics educationEngineering

Abstract

fetched live from OpenAlex

There is now ample evidence that teachers tend to make substantial modifications to both the prescribed content and methods of the curricula they administer, and that such modifications are likely to attenuate curricula effects. We examine the fidelity with which teachers implement "Protecting You, Protecting Me," an underage alcohol use prevention curriculum. Findings suggest that while teachers attempted to implement most sections of a lesson, the lessons taught were consistently--and often extensively--adapted. We conclude that since teachers are likely to continue to modify lessons, curriculum developers and trainers should enhance their understanding of how prevention curricula are taught under real world conditions, help teachers to reinforce key curriculum concepts, and consider modifying those curricular sections that teachers are adapting with greatest frequency.

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)0.029
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.136
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.276
GPT teacher head0.660
Teacher spread0.384 · 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 designObservational
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

Citations35
Published2006
Admission routes1
Has abstractyes

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