Testing the efficacy of a narrative short film in educating the public about providing emotional support to individuals with fertility problems
Bibliographic record
Abstract
Background To educate the public on how best to support people with fertility problems, a narrative short film “Ten Things Not to Say to Someone Struggling with Infertility” was created, depicting the impact that helpful versus unhelpful dialogue has on someone with fertility problems.Methods Before and after watching the video, 419 participants from the public were presented with a hypothetical vignette describing a woman experiencing fertility problems and asked about the likelihood that they would endorse a series of helpful and unhelpful statements when communicating with the protagonist. Pre and post endorsement of helpful versus unhelpful statements were compared, as were self-perceived knowledge about the mental health aspects of fertility problems, confidence in providing emotional support to someone with fertility problems, and empathy for the protagonist.Results Participants endorsed fewer unhelpful statements after the video relative to before (M(SD) = 2.2(2.3) vs. 1.3(2.3), p < .001) and fewer participants endorsed at least one unhelpful statement (72% to 47%, p < .001). Self-perceived knowledge of fertility problems, confidence in providing support, and empathy increased at post-test (ps < .001; Cohen’s d = .56–.83) indicating medium-large effects.Conclusions A narrative short film appears to be an effective dissemination strategy for sensitizing the public to the emotional struggles of individuals experiencing fertility problems.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".