Bibliographic record
Abstract
Negative age-stereotypes can foster biopsychosocial health consequences within older adults; however, there is a paucity of research examining self-efficacy (a determinant of activity avoidance or uptake), and no research exploring stair navigation performances after exposure to age-stereotypes. The present work aimed to examine the influence of age-stereotype priming on stair navigation, while exploring self-efficacy as a moderator in older adults. \nOlder adults aged 50 years or greater were voluntarily recruited within the Greater Toronto Area. In total, 130 older adults participated, of which 90 represented the healthy sub-sample and 40 represented those with osteoarthritis. Psychological questionnaires and stair navigation assessments were completed after exposure to age-stereotype primes. \nThe following manuscripts act as independent and connected segments toward examining stereotype priming, stair navigation, and self-efficacy in older adults. Manuscript one illustrates the development of effective stereotype primes and statistical models for analyzing the effects of age-stereotypes on stair navigation and self-efficacy for stairs. The results confirmed variables and statistical methodologies to be used in further analyses, with implications for streamlining future research. Stemming from this work, manuscript two and three employ these models to examine the influence of age-stereotype priming on self-efficacy for stairs in healthy older adults from baseline to post-exposure, as well as the influence of priming on stair navigation. The results suggested negatively primed older adults experienced declines in self-efficacy for stairs, while displaying slower stair navigation compared to those positively primed. The implications speak to developing priming as an intervention tool to mitigate negative primes and enhance task-specific functionality and well-being in healthy older adults. \nBuilding from these results, manuscript four compared the influence of stereotype priming on self-efficacy for stairs and stair navigation among healthy adults and those with osteoarthritis, the most common age-related condition. The results suggested a complex relationship regarding prime exposure and the health status of older adults. These findings have implications for developing primes for special populations to facilitate performance enhancements. \nOverall, these findings have implications for promoting stereotype research efficiency, while acknowledging the limitations that remain to be studied by future stereotype research regarding healthy adults and those with chronic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".