The feasibility and acceptability of an inoculative intervention video for gambling advertising: A focus group study of academics and experts-by-experience
Bibliographic record
Abstract
Background: Gambling advertising employs a range of persuasive strategies, yet few interventions have been established to foster resilience against the commercial tactics of the gambling industry. There is a consequent need for these ‘counter-advertising’ interventions that are developed independently from the gambling industry, and that incorporate the input of experts with lived experience of gambling-related harm (Experts-by-Experience; EbyE). Aims: We therefore aimed to evaluate the feasibility and acceptability of a counter-advertising intervention video to increase resilience to gambling advertising persuasion. Methods: Three in-depth focus groups were conducted, and each group contained a mixture of gambling-related academics (N = 12) and EbyEs (N = 10). Participants were given access to the intervention video, and subsequently provided their perceptions and feedback during the focus groups. Qualitative data were audio recorded, transcribed, and thematically analysed by the research team via an inductive approach. Results: Three main themes were identified. Firstly, participants recommended a shorter video that had a simplified and digestible structure. Second, frequent real-world examples of gambling advertisements within the video were discouraged, and the inclusion of a relatable human voiceover was considered imperative to the receptiveness of the video. Finally, participants deemed it important to deliver psychologically grounded yet jargon-free content via a conversational style. An overall narrative framed by consumer-protection was also preferred in order to increase acceptance of the video content, rather than a more didactic framing. Conclusions: Evaluating the acceptability of a counter advertising intervention video provided valuable insight from both an academic and lived-experience perspective. Such insight is instrumental to the meaningful co-design of counter-advertising interventions.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".