Effect of low-intensity group exercises (Neuro walks) on mood and general wellbeing for service users with Complex Brain Injury at the Inpatient Rehabilitation Centre
Bibliographic record
Abstract
Purpose and relevance:The purpose of this project was to evaluate the effectiveness of the initiative (Group Exercises-Neuro Walks) on mood and general wellbeing among Neuropsychiatry inpatients with complex brain injury. The benefits of exercise post-ABI are substantial; however, most ABI survivors do not participate in the minimum recommended amount (Reavenall & Blake, 2010). Methodology:Neuro Walks – group exercises were introduced on three inpatient wards. This project used a quantitative method. Changes in mood were measured by comparing self-reported data on the Ottawa Mood Scale prior to and post each Neuro Walk session. Changes in participants’ general wellbeing were measured by comparing session attendance and amount of leave before and after. Results and conclusion:The repeated measures ANOVA revealed a significant main effect of exercise on mood scores. The paired sample t-test revealed a significant increase in session attendance and the amount of patient leave. The findings suggest that engaging in low-intensity group exercise leads to immediate improvements in mood, increased session attendance, and patient leave off the ward. This is consistent with previous research (Saunders et al., 2014; Norris et al., 2013). Limitations of the project included the lack of a control group and the use of self-report measures for assessing mood. Future research could explore the long-term effects of exercise on mood and investigate potential moderators of the exercise-mood relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".