Hawthorne effect on surgical studies
Bibliographic record
Abstract
BACKGROUND: The Hawthorne effect or 'observer effect' describes a change in normal behaviour when individuals are aware they are being observed. This may have an impact on effect estimates in clinical trials. The purpose of this study was to determine if the Hawthorne effect had been recorded as a risk of bias in surgical studies. METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses compliant literature search was conducted till March 2019. Eligible studies included those reporting or not reporting the Hawthorne effect in surgical studies from the following databases: MEDLINE, Embase, CINAHL, AMED, BNI, HMIC, PsycINFO, Web of Science, Cochrane Library, Google Scholar and OpenGrey. Two reviewers independently reviewed the papers, extracted data and appraised study methods using the Newcastle Ottawa Scale or the Cochrane risk of bias tool. Data were analysed descriptively. RESULTS: A total of 842 papers were identified, of which 16 were eligible. Six (37%) observational studies were identified with the aim of measuring the Hawthorne effect on their outcome with five reporting that the Hawthorne effect was responsible for the improvements in outcomes and one reporting no change in outcome due to the Hawthorne effect. Ten (63%) studies were identified, of which eight used the Hawthorne effect as an explanation to improvements seen in the control group or their secondary outcomes and two to compare their results with other studies. CONCLUSION: There is considerable between-study heterogeneity on how the Hawthorne effect relates to surgical outcomes. Further consideration on reporting and considering the importance of the Hawthorne effect in the design of surgical trials is warranted.
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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 | 0.440 | 0.700 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".