Development of radio dramas for health communication pilot intervention in Canadian Inuit communities
Bibliographic record
Abstract
A mixed-methods approach was used to develop a culturally appropriate health intervention over radio within the Inuit community of Pangnirtung, Nunavut (NU), Canada. The radio dramas were developed, recorded and tested pre-intervention through the use of Participatory Process and informed by the extended elaboration likelihood model (EELM) for education-communication. The radio messages were tested in two focus groups (n = 4 and n = 5) to determine fidelity of the radio dramas to the EELM theory. Focus group feedback identified that revisions needed to be made to two characteristics required of educational programmes by the EELM theorem: first, the quality of the production was improved by adding Inuit youth recorded music and second, the homophily (relatability of characters) of radio dramas was improved by re-recording the dramas with voices of local youth who had been trained in media communication studies. These adjustments would not have been implemented had pre-intervention testing of the radio dramas not taken place and could have reduced effectiveness of the overall intervention. Therefore, it is highly recommended that media tools for health communication/education be tested with the intended target audience before commencement of programmes. Participatory Process was identified to be a powerful tool in the development and sustainability of culturally appropriate community health programming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".