Temporal Focus as a Motivator of Self-Directed Change among People Engaging in Problem Gambling Behaviour
Bibliographic record
Abstract
Motivational strategies to facilitate behaviour change typically focus people living with addiction on a better possible future should the addictive behaviour be reduced or ceased. In contrast, emerging research suggests focusing people on positive aspects of the life one lived before addiction also has behaviour change utility. In the current program of research, I measured and manipulated temporal focus to systematically assess the process by which a focus on one's past or future motivates self-directed change as well as the limits and boundaries of each approach. In Study 1, a qualitative assessment of the meaning people living with problem gambling (N=57) assign to their past before gambling and future without gambling revealed two distinct types of past (i.e., positive versus difficult) and anticipated future (i.e., optimistic versus ambivalent) experiences. Those with a positive past or optimistic future reported being more ready to change their gambling behaviour compared to those with a difficult past or ambivalent future. In Study 2 (correlational), problem gamblers (N=229) who perceived their past before gambling to be more positive were more ready to change their behaviour to the extent that they felt nostalgic for their pre-addicted past. Moreover, participants who felt optimistic about their future without gambling were more ready to change to the extent that they felt a sense of longing for this better possible future. In Study 3, participants (N=273) manipulated to focus on a positive past (versus an ordinary past) reported greater nostalgia for the pre-addicted past, which was associated with greater readiness to change. In Study 4, participants (N=174) manipulated to focus on a positive future (versus an ordinary future) reported greater longing for a future without gambling, which was associated with greater readiness to change. Lastly, an integrated data analysis testing the relative efficacy of a positive past and positive future in promoting readiness to change revealed no between-condition differences. Many people living with problem gambling are resistant to behaviour change. This program of research has significance by demonstrating when and for whom self-directed change can be advanced by way of focusing on a positive past and better possible future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".