Beyond a good story: from Hawthorne Effect to reactivity in health professions education research
Bibliographic record
Abstract
CONTEXT: Observational research is increasingly being used in health professions education (HPE) research, yet it is often criticised for being prone to observer effects (also known as the Hawthorne Effect), defined as a research participant's altered behaviour in response to being observed. This article explores this concern. METHODS: First, this article briefly reviews the initial Hawthorne studies and the original formulation of the Hawthorne Effect, before turning to contemporary studies of the Hawthorne Effect in HPE and beyond. Second, using data from two observational studies (in the operating theatre and in the intensive care unit), this article investigates the Hawthorne Effect in HPE. RESULTS: Evidence of a Hawthorne Effect is scant, and amounts to little more than a good story. This is surprising given the foundational nature of the Hawthorne Studies in the social sciences and the prevalence of our concern with observer effects in HPE research. Moreover, the multiple and inconsistent uses of the Hawthorne Effect have left researchers without a coherent and helpful understanding of research participants' responses to observation. The authors' HPE research illustrates the complexity of observer effects in HPE, suggests that significant alteration of behaviour is unlikely in many research contexts, and shows how sustained contact with participants over time improves the quality of data collection. CONCLUSION: This article thus concludes with three recommendations: that researchers, editors and reviewers in the HPE community use the phrase 'participant reactivity' when considering the participant, observer and research question triad; that researchers invest in interpersonal relationships at their study site to mitigate the effects of altered behaviour; and that researchers use theory to make sense of participants' altered behaviour and use it as a window into the social world. The term 'participant reactivity' better reflects current scientific understandings of the research process and highlights the cognitive work required of participants to alter their behaviour when observed. Perhaps the most important lesson to be learned from the original Hawthorne experiments is the power of a good story (Levitt & List, 2011).
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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 | 0.363 | 0.650 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.021 | 0.045 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.019 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".