Are biased perceptions of exercise modifiable? A case study in reframing for women with Multiple Sclerosis
Bibliographic record
Abstract
Cognitive Reframing is a novel exercise coaching strategy used to reduce a person’s negative or biased feelings toward exercise. Evidence in the general population suggests that Cognitive Reframing is effective. Multiple Sclerosis (MS) symptoms may magnify a person’s barriers to exercise, increasing their biased perceptions. Prior to implementing counseling strategies into new populations, they must first be pilot tested to understand how it is received and whether the protocol needs to be tailored to the new population. The aim of this research was to understand the experiences with and feasibility of Cognitive Reframing for individuals with MS. This mixed-method study included seven women (Age range 29-48) who completed a reframing session (~15 mins), pre-post descriptive survey measures of cognitive biases and self-efficacy, and a follow-up interview about their experience receiving Cognitive Reframing. Data were analyzed using descriptive statistics and thematic analysis. Three participant case studies were created to illustrate different experiences with Reframing. Themes derived from follow-up interviews include increased motivation to be physically active and an increased level of self-reflection about their inaccurate views of exercise. One case, Maria experienced a decrease in their cognitive biases (Mpre=8.0 vs Mpost=4.43) and indicated struggling less with exercise during the follow-up. Participants suggested modifications to adapt Reframing to MS Populations. Findings will provide insight on how to tailor and acceptably deliver reframing coaching for individuals with MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".