Are current elicitation techniques for barriers and enablers confounded with motivation? How natural language may hinder theory‐guided research
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to compare standard elicitation techniques for barriers and enablers for physical activity and sleep behaviours, to an alternative approach whereby participants were told to only consider the literal meanings of the words prevent/enable. DESIGN: Randomized controlled design. METHODS: College students were randomized to either a standard methods group (n = 177) (what prevents you from doing behaviour X) or a vignette group (n = 176) to encourage them to think of the literal meaning of the words prevent/enable. Responses were then codified by two blinded researchers. RESULTS: Students reported significantly different types of control beliefs between groups. Those in the standard group reported significantly more overall beliefs (p's < .05, except sleep/enable), suggesting poorer discrimination in interpreting what was meant by 'prevent' and 'enable'. CONCLUSIONS: This study demonstrates when self-efficacy-related control beliefs are elicited, natural language words such as 'prevent' and 'enable' have the potential to confuse people about the intent of the question.
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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 | 0.490 | 0.604 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".