The effects of gain-framed messages on sport intention and activity in middle aged adults
Bibliographic record
Abstract
Gain-framed messages (Rothman & Salovey, 1997) emphasizing benefits of sport represent one possible means to get adults physically active (PA). This experiment investigated the effects of an online sport gain-framed message video and sport possible self protocol (e.g., Murru & Martin Ginis, 2010) on indices of sport intention and activity. 244 adults (M = 50.5 yrs, rge = 40-59) completed baseline/screening measures before randomized assignment (T1), an experimental/control intervention one week later (T2), and follow-up measures (T3) four weeks after T2. Participants watched either a video espousing common involvement opportunities in adult sport (experimental; Young & Medic, 2011) or completed a PA quiz (control), before completing a sport possible self protocol. Participants reported sport intention (T1, T2, T3), sport activity (T1 and T3 using moderate-strenuous items on GLTEQ; Godin & Shephard, 1985), requests for an adult sport newsletter (T2), and recent registration in a sport program (T3). Results showed that the experimental group requested more newsletters (p = .03) immediately after the intervention, and reported having registered for more sport programs (p = .03). ANCOVA results for GLTEQ scores failed to show a group by time interaction (p = .74). For intention, an interaction (p = .06) between group and time depended on whether participants were high/low on approach motivation (BAS Drive; Carver & White, 1994). Specifically, experimental conditions showed benefits from T1 to T2 for persons with low BAS Drive, whereas control conditions facilitated improved intentions from T1 to T2 for persons with high BAS Drive, ps ≤ .001.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".