Examining the effects of gain-framed messages on the activation and elaboration of sport possible selves in middle aged adults
Bibliographic record
Abstract
Mechanisms involved in the reception of gain-framed messages (Rothman & Salovey, 1997) are still relatively unknown (Gallagher & Updegraff, 2012). One mechanism that may influence how messages are received and translated into behaviour change is hoped-for possible selves (Markus & Nurius, 1986). This experiment examined the possibility that an online gain-framed message video that highlighted nine benefits of adult sport participation (Young & Medic, 2011) may elicit hoped-for possible selves regarding future sport behaviours. 182 participants aged 40 to 59 (M = 50.9, SD = 5.4) were randomly assigned to watch either the video (experimental) or complete a physical activity quiz (control). Afterwards, participants were asked if their respective task activated a sport hoped-for possible self, and they described aspects of the possible self in writing. Descriptive responses were analyzed qualitatively to determine whether participants identified a possible self, and to enumerate meaning units (Tesch, 1990) within the descriptions. Results revealed that the experimental group more frequently described a possible self than the control group, χ2(1, N = 176) = 9.1, p = .003, phi = .24. Meaning unit analyses showed that the experimental group elaborated more on their possible selves, t(94) = 3.1, p = .003, η2 = .09, and also included more meaning units relating to the delay of aging, χ2(1, N = 96) = 4.6, p = .03, phi = .26, and social factors, χ2(1, N = 96) = 4.6, p = .03, phi = .24, in their possible self descriptions in comparison with the control group.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".