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Record W2099403866 · doi:10.1177/0017896910386266

Peer sexual health education

2010· article· en· W2099403866 on OpenAlexaff
Gobika Sriranganathan, Denise Jaworsky, June Larkin, Sarah Flicker, Lisa Campbell, Susan Flynn, Jesse Janssen, Leah Erlich

Bibliographic record

VenueHealth Education Journal · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsYork UniversityUniversity of British ColumbiaWestern UniversityPlanned Parenthood TorontoToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsPeer educationReproductive healthHealth educationMedical educationHealth promotionDiversity (politics)Program evaluationPsychologyPeer groupMedicinePublic relationsNursingPolitical sciencePublic healthEnvironmental healthPopulationSocial psychology

Abstract

fetched live from OpenAlex

Peer education is used as a health promotion strategy in a number of areas, including sexual health. Although peer education programmes have been around for some time, published systematic evaluations of youth sexual health peer education programmes are rare. This article discusses the advantages and disadvantages of youth sexual health peer education programmes, the importance of programme evaluation, and strategies for developing effective programme evaluation tools. The value of conducting both process (programme delivery) and outcome (programme impact) evaluation is examined as well as methods for conducting these forms of assessment. Considering the wide range of peer education programmes and the diversity of communities served, the article concludes that the creation of a single evaluation method may be an impossible task. To address this challenge, principles for effective programme evaluation are proposed with tools that can be tailored to the unique goals of specific sexual health organizations.

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)0.010
metaresearch head score (Gemma)0.035
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.003

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 head0.572
GPT teacher head0.736
Teacher spread0.164 · 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

Citations48
Published2010
Admission routes1
Has abstractyes

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