Using a video to introduce fertility preservation to women with cancer.
Bibliographic record
Abstract
e18021 Background: Women with cancer risk premature ovarian insufficiency with implications for future fertility due to chemotherapy and radiotherapy. Still, infertility discussions are inconsistently provided. Options for egg cryopreservation exist for females, but discussing these topics can be overwhelming for patients and families. This paper aims to assess the understandability, actionability, and readability of a fertility preservation (FP) educational video, as well as patient (pt) and caregiver pre- and post-video perceptions and knowledge. Methods: A video was created by the institution's Adolescent and Young Adult Program to explain relevant anatomy, pathophysiology of ovulation and process of cryopreservation. Understandability and actionability were evaluated using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-AV) and readability using the SMOG and Flesch-Kincaid indices. Pt perceptions and knowledge growth were captured using pre-post questionnaires. Female pts (n = 108) were recruited in oncology clinics over 2 months, using a convenience sample. Questionnaire responses were analyzed using SPSS to calculate descriptive statistics to conduct correlation analyses. Results: The median age of the participants was 28 (range 14-39 yrs). The average PEMAT-A/V score was 79% (±11.3%) for understandability and 72% (±13.1%) for actionability. A score of 70% is regarded as acceptable. The readability assessment determined that the video script was, on average, at a grade 8 reading level. Pts’ interest in learning about FP increased, with 14% of those initially uninterested or unsure wanting to learn more after viewing the video. Pts’ general knowledge on FP increased from the pre- to post-video questionnaire, from a mean score of 75% initially (±17.9%), to a mean score of 84% (±14.5%) after watching the video (t = -5.972, p = 0.000). On average, overall satisfaction with the video was 85% (±8.5%). Conclusions: Women commonly use online tools for researching health questions. This study demonstrates that the video is understandable and provides guidance for future discussion. It shows that videos can spark interest in sensitive discussions and can improve fertility knowledge. This video may encourage providers to more consistently initiate discussions about infertility. Furthermore, the video can be translated for related topics to help provide accurate information in a patient-friendly medium.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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".