You need to see the world in order to measure it: The importance of a high follow‐up rate
Bibliographic record
Abstract
In their letter 1, Bedendo & Noto question the use of incentives in clinical trials and argue that their use does not allow one to study the effectiveness of internet-based brief interventions. They call for studies to sacrifice low attrition and not provide incentives to assess the effectiveness of interventions in real-life scenarios. We think this approach is prone to a major risk of bias. High attrition rates threaten study validity and may artificially inflate intervention effects 2. The simple fact of observing individuals in a research study is known to introduce possible bias (the ‘Hawthorne effect’). As such, no research study can truly assess what would happen to subjects outside the research context. A high follow-up rate was a priority for our study, and therefore we offered incentives to participants 3. As stated by Bedendo & Noto, providing incentives to increase follow-up rates in clinical trials may attract participants more interested in the incentive than in the research, and may lead to participants rushing through the study questionnaires or the intervention's content. Even if we assume this is the case, the results would most probably be biased towards the null. In addition, in our study, participants did not receive money but a coupon to download music online (the equivalent of one pop album) at the end of the follow-up period, which is less likely to attract participants willing to participate ‘only for money’. As such, the observed effects in a study with incentives and a high follow-up rate probably represent what can be expected if some people participate who are not willing to complete the intervention per se. Indeed, limiting research participation to those interested enough in the intervention to go through time-consuming research procedures without being compensated for it is likely to attract individuals who are more interested in the intervention than the general public. In the long term, we think that the risk of overestimating intervention effects is more problematic than the use of incentives (at least those used in our study). It is our opinion that no study should sacrifice low attrition rates. The impact of incentives on attrition rates and the profile of participants in the specific context of internet trials could be studied to determine whether and how it may influence the study results, whether different forms of incentives (money, coupons) have different effects and whether these effects may differ by culture and countries. The present letter addresses comments made on a study conducted by N.B., B.B. and J.A.C.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".