Cognitive Training Intervention for Heart Failure Patients: A Randomised Controlled Trial
Bibliographic record
Abstract
Objectives The aim of this study was to evaluate the impact of ‘Cognitive Training’ on cognitive functions, self-care, medication adherence, QOL, functional capacity and satisfaction level among HF patients. Materials and Methods In the current randomised controlled trial, 60 HF patients were enrolled from the cardiology outpatient department by total enumeration sampling technique and randomised into the 30 control and 30 experimental group participants using a random number table. A socio-demographic cum clinical profile sheet, Montreal cognitive assessment, self-care of HF index, Hill-Bone medication adherence scale, Minnesota living with HF questionnaire (MLHFQ), 6-min walk test and satisfaction questionnaire were used for collecting data and pre-test was done at the time of enrolment. The experimental group participants received cognitive training through face-to-face counselling sessions provided by the researcher along with routine care. Control group participants received routine care. Post-test was done at the end of the 12th week to assess the impact of Cognitive training on cognitive functions and other variables. Descriptive and inferential statistics were used to analyse the data. Results Significant improvement was observed in cognitive functions which include memory (P < 0.001), executive functions (P < 0.001) and attention and concentration (P < 0.001) among the experimental group participants in the 12th week. Furthermore, significant improvement was observed in self-care maintenance (P < 0.001), self-care management (0.04), self-care confidence (P < 0.001), medication adherence (P < 0.001), QOL (P < 0.001), functional capacity (0.002) as well as satisfaction with care provided. Conclusion Cognitive training was found to be effective in terms of improvement in cognitive functions and it should be part of the routine intervention in healthcare settings for better patient outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".