Bibliographic record
Abstract
The intention-behaviour gap arises when people fail to convert their intentions into behaviour.Although limited research has sought to determine if impulsivity as an individual difference moderates the intention-behaviour gap, the issue has been examined exclusively in the context of established behaviour.Given that features of impulsivity pertain to seizing opportunities with little regard to the consequences, seeking novelty, and welcoming new and exciting experiences, this dissertation focused on determining whether impulsivity moderated the intention-behaviour gap for new behaviour.In Study 1, a total of 51 participants took part in online semi-structured interviews to qualitatively assess how they reported experiencing the intention-behaviour gap for new behaviour.Thematic analysis identified two main themes related to participants' perceptions and experiences about the intention-behaviour gap and new behaviour.Participants with high impulsivity (n=26) reported a greater preference for new behaviour and for following-through on their intentions for new behaviour than participants with low impulsivity (n=25).In Study 2, a longitudinal observational design was conducted (n=405) to explore whether impulsivity moderated the intention-behaviour gap for new engagement in virtual fitness.Consistent with the hypothesis, among participants high in impulsivity, greater intentions were more strongly linked to the likelihood of engaging in virtual fitness at least once during a 7-day period, whereas for participants low in impulsivity, greater intention was not associated with new virtual fitness engagement.This moderation effect was extended to Study 3 using an experimental design.Participants (n=436) who were randomly assigned to view a persuasion message (n=219) to increase their intention to engage in a new virtual fitness behaviour (versus control condition, n=217) reported significantly higher engagement in virtual fitness than participants low in impulsivity.Secondary exploratory analyses revealed that lack of premeditation and sensation seeking moderated the effect of intention on new virtual fitness engagement, suggesting that certain facets of impulsivity may play a moderating role in the intention-behaviour relation.Overall, this research demonstrated that impulsivity-an individual difference usually associated with maladaptive outcomes, negative consequences, and health risk behaviours-moderated the intention-behaviour relation for a new health-promoting behaviour among young adults.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".