Assessing knowledge translation following a pre-cancer diagnosis: a multinational evaluation of online resources targeting patients with cervical dysplasia
Bibliographic record
Abstract
BACKGROUND: Distressful clinician-to-patient dialogue such as a pre-cancer diagnosis of cervical dysplasia may interfere with information retention. Patient education material provided as an online resource offers a suitable option to review relevant health information outside the clinic. The aim of this study was to evaluate online resources (ORs) affiliated with healthcare institutions across Australia and the United Kingdom (UK) on their effectiveness to translate accessible and current knowledge to patients referred for loop electrosurgical excision procedure (LEEP) treatment. METHODS: A comprehensive directory of ORs related to LEEP was compiled from public hospital websites across Australia and the UK. Quantitative and qualitative methods were applied to evaluate resource reading-level (measured using three validated readability indices); actionability and understandability (measured using the Patient Education Material Assessment Tool [PEMAT]); and content (described using content analysis to assess disclosure practices associated with LEEP-related complications). RESULTS: All ORs (n = 39) exceeded the recommended reading level (Australia: x̄ = 10.07, σ = 1.01; UK: x̄ = 10.17, σ = 0.96). PEMAT results indicated higher percentages of ORs scored as understandable (Australia: 50.0%; UK: 69.7%) versus actionable (Australia: 33.3%; UK: 6.1%). Content analysis revealed widespread discordance in the disclosure of longer-term LEEP complications associated with pregnancy, fertility, and sexual function in both countries. CONCLUSIONS: Disclosures with significant health and wellness implications should be made with clear reference to peer reviewed science. Wider application of purpose-designed health literacy tools could improve measures of readability, actionability and understandability. International collaborations may provide opportunities to develop more comprehensive and patient-centred education materials to improve provider-to-patient knowledge translation.
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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 | 0.024 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".