Adaptive Causal Thinking About Mobility Challenges: Implications for Quality of Life
Bibliographic record
Abstract
Abstract Weiner’s attribution theory posits that it is adaptive to ascribe challenges to controllable causes (e.g., insufficient effort, bad strategies) and maladaptive to ascribe them to uncontrollable causes (e.g., old age). This is supported by our prior research that showed a heightened risk of mortality when mobility challenges were attributed to old age. The present pilot study randomly assigned older adults (N=36) in a day hospital to either an attributional retraining (AR) intervention group that viewed a video intended to shift causal thinking regarding mobility challenges (uncontrollable→controllable causes), or to a comparison group (No-AR). Participants completed a Time1 survey, the AR intervention (one week later), and a Time2 follow-up survey two weeks later. A manipulation-check revealed that AR was effective in shifting causal thinking away from maladaptive causes; a decline in the endorsement of the old age attribution was observed in the AR group (Ms=2.61 vs. 2.06; p=.02), but not in the No-AR group (Ms=2.45 vs. 2.35, p=.30). The AR and No-AR groups were equivalent at Time 1 on two quality-of-life outcomes: helplessness and perceived control (PC) over health. However, helplessness declined (Time1-Time2) in the AR group (Ms=1.13 vs. 0.73, p=.03), whereas it was relatively stable in the No-AR group (Ms=1.42 vs. 1.26, p>.20). Moreover, PC increased marginally in the AR group (Ms=6.50 vs. 6.69; p=.06), but declined in the No-AR group (Ms=6.20 vs 5.45, p=.05). Together, these findings suggest that attributions can be shifted away from uncontrollable causes and that this shift can have a protective effect that benefits quality-of-life.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".