Quality of information about success rates provided on assisted reproductive technology clinic websites in Australia and New Zealand
Bibliographic record
Abstract
BACKGROUND: Many factors influence the chance of having a baby with assisted reproductive technologies (ART). A 2016 Australian Competition and Consumer Commission (ACCC) investigation concluded that ART clinics needed to improve the quality of information they provide about chance of ART success. AIM: To evaluate changes in the quality of information about success rates provided on the websites of ART clinics in Australia and New Zealand before and after the ACCC investigation. MATERIALS AND METHOD: Desktop audits of websites of ART clinics in Australia and New Zealand were conducted in 2016 and 2017 and available information about success rates was scored using a matrix with eight variables and a possible range of scores of 0-9. RESULTS: Of the 54 clinic websites identified in 2016, 32 had unique information and were eligible to be audited. Of these, 29 were also eligible to be audited in 2017. While there was a slight improvement in the mean score from 2016 to 2017 (4.93-5.28), this was not statistically significantly different. Of the 29 clinics, 14 had the same score on both occasions, 10 had a higher and five a lower information quality score in 2017. CONCLUSIONS: To allow people who consider ART to make informed decisions about treatment they need comprehensive and accurate information about what treatment entails and what the likely outcomes are. As measured by a scoring matrix, most ART clinics had not improved the quality of the information about success rates following the ACCC investigation.
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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 | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".