Think You Can Shrink? A Proof-of-Concept Study for Men’s Health Education Through Edutainment
Bibliographic record
Abstract
Connecting people to useful, actionable health resources is a substantive challenge that sits at the heart of health communication. Digital media provides means of producing, distributing and revising content and creates possibilities for new and multiple channels for reaching and engaging audiences, particularly when combined with social media. While there is much promise of digital media forms to deliver audiences and promote engagement, the health communication landscape is still largely hit-and-miss with few 'best practice' examples to follow. Proof-of-concept studies allow for a structured, focused exploration of ways to leverage the potential of digital media and learn what approaches have the promise to invest resources in amid a sea of possible options. Think You Can Shrink? (TYCS) is a multi-episode web series modelled on a reality TV show format. The show's key objective is to educate men and demonstrate, through modelling, ways men can support other men to encourage help-seeking behaviours and greater health communication, which in turn, may also lead to better health outcomes. Given the newness of the approach, the project was launched as a proof-of-concept study to explore: (a) whether this approach could engage the interest of men, (b) what initial impact this approach might induce and
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 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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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.
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".