Self-Reevaluation and Anticipated Regret Did Not Change Attitude, Nor Perceived Distance in an Online Context
Bibliographic record
Abstract
Internet-delivered interventions can be effective in changing behaviour, but more research is needed on effective elements of behaviour change interventions. Moreover, although anonymity is one of the advantages of using an online context, it might also increase the perceived distance between the participant and the intervention. Hence, the current study investigated whether the behaviour change methods of self-reevaluation and anticipated regret can be used to narrow the perceived distance and, ultimately, foster attitude change. A 3x3 factorial between-persons design with an additional control group was used (N=466), resulting in a total of 10 conditions (n’s ranging from 43-49). The first factor manipulated is assessment of self-image; cognitive, affective, or the combination of both. The second factor manipulated is behavioural focus; self-image with behaviour, without behaviour or both with and without behaviour. Post-test measurements were conducted immediately after the manipulation. The key finding of the current study is that the behaviour change methods of self-reevaluation and anticipated regret did not have an impact on changes in attitude towards oral contraceptive use, nor on the distance perceived by participants. Despite the null results, the current study contributes to the body of evidence regarding self-reevaluation and anticipated regret, which can be integrated in meta-regressions of experimental studies to advance behaviour change theory.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".