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Record W2098547020 · doi:10.1177/1524839905278889

Developing Internet-Based eHealth Promotion Programs: The Spiral Technology Action Research (STAR) Model

2006· article· en· W2098547020 on OpenAlexaff
Harvey A. Skinner, Oonagh Maley, Cameron D. Norman

Bibliographic record

VenueHealth Promotion Practice · 2006
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaUniversity Health NetworkOntario Tobacco Research UnitUniversity of Toronto
Fundersnot available
KeywordseHealthHealth promotionThe InternetPromotion (chess)Public relationsAction planInformation and Communications TechnologyAction researchHealth educationKnowledge managementBusinessMedicineComputer scienceHealth careWorld Wide WebSociologyPolitical sciencePublic healthNursingManagementPedagogy

Abstract

fetched live from OpenAlex

Health education and health promotion have a tradition of using information and communication technology (ICT). In recent years, the rapid growth of the Internet has created innovative opportunities for Web-based health education and behavior change applications-termed eHealth promotion. However, many eHealth promotion applications are developed without an explicit model to guide the design, evaluation, and ongoing improvement of the program. The spiral technology action research (STAR) model was developed to address this need. The model comprises five cycles (listen, plan, do, study, act) that weave together technological development, community involvement, and continuous improvement. The model is illustrated by a case study describing the development of the Smoking Zine (www.SmokingZine.org), a youth smoking prevention and cessation Web site.

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)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.006
Scholarly communication0.0050.008
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.343
GPT teacher head0.566
Teacher spread0.224 · 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 designQualitative
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

Citations100
Published2006
Admission routes1
Has abstractyes

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