Using normative messages to increase healthy behaviours
Bibliographic record
Abstract
Purpose The purpose of this research is to evaluate the impact of theory‐based poster messages on stair‐climbing behaviour in a work environment. The highest‐rated poster developed by the Public Health Agency of Canada's stairway to health program was used as a comparison condition. Design/methodology/approach Naturalistic observation of stair traffic was conducted in order to measure the effectiveness of poster prompts on stair‐climbing behaviour. Over a period of three years, three separate studies were conducted aimed at increasing stair‐use via experimentally manipulated and theory‐based poster messages. Findings Results suggest that messages derived from a norm‐based framework are more persuasive than generic information‐based posters when attempting to increase stair‐climbing behaviour. Practical implications Small increases in health‐related behaviours at work have important consequences for both individuals and organizations. Using poster messages derived from social psychological theory could prove advantageous for practitioners attempting to increase healthy behaviours at work. Originality/value This research provides the first evidence for the use of norm‐based health‐related messages targeted at increasing healthy behaviours. The study makes a theoretical contribution to the creation and application of norm‐based appeals using both simple and complex message framing. Prior to this study, there was no available research on the effectiveness of such appeals on health‐related behaviour.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".