Quality of information about success rates provided on assisted reproductive technology clinic websites in Australia and New Zealand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it