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Record W2146553776 · doi:10.22230/cjc.2007v32n3a1966

Communicating Health Information: The Community Engagement Model for Video Production

2007· article· en· W2146553776 on OpenAlexafffundvenueabout
David Murphy, Ellen Balka, Iraj Poureslami, Diana E. Leung, Anne‐Marie Nicol, Trent Cruz

Bibliographic record

VenueCanadian Journal of Communication · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsWestern UniversityUniversity of British ColumbiaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCitizen journalismProduction (economics)Process (computing)Computer scienceParticipatory designCommunity engagementBusinessAdvertisingPublic relationsProcess managementKnowledge managementMultimediaPolitical scienceEngineeringWorld Wide WebOperations management

Abstract

fetched live from OpenAlex

The Community Engagement Model was developed as a tool for the production of health communication videos for broadcast on local television stations. The model, a hybrid of participatory video design and social marketing techniques, uses iterative design principles for both production and evaluation. This article reports on the use of this model for the design and production of a series of videos aimed at promoting awareness of the BC NurseLine (a 24-hour telephone health service) among Farsi speakers in the Greater Vancouver area. Statistical analysis of project-related data suggests that the use of an extensive, culturally engaged process to produce and evaluate the videos was integral to its success. The steps taken in this campaign are described to show how the Community Engagement Model can be used to produce effective, culturally sensitive, participatory media targeted at specific communities.

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)0.013
metaresearch head score (Gemma)0.028
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 score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.005
Scholarly communication0.0080.007
Open science0.0030.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.104
GPT teacher head0.294
Teacher spread0.189 · 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

Citations11
Published2007
Admission routes4
Has abstractyes

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