Protective Factors Influencing Cognitive Function Among Middle-Aged Adults in Pakistan
Bibliographic record
Abstract
Background: The rapid increase in the aging population and the critical role of cognitive functioning in successful aging have shifted scholarly focus toward identifying its risk and protective factors. This study aimed to examine the protective correlates of cognitive functioning among middle-aged adults in Pakistan. Methodology: A correlational research design was employed, and purposive sampling was used to recruit participants. The sample size of 140 middle-aged adults (males = 43, females = 97), aged 35–55 years (M = 44.23, SD = 7.1), was calculated using G-power analysis. Data collection tools included the Montreal Cognitive Assessment (MoCA), the Islamic Practices subscale, and a demographic questionnaire. Statistical analyses were conducted using SPSS version 23. Results: Cognitive functioning was positively associated with education, number of friends, and participation in religious activities. In contrast, negative relationships were observed with age, number of children, obesity, hypertension, and comorbidities. Regression analysis indicated that education, social connections, and religious participation were significant positive predictors of cognitive functioning, while age and obesity emerged as significant negative predictors. Conclusion: The findings suggest that various modifiable protective factors can enhance cognitive functioning. These insights emphasize the importance of addressing modifiable factors to mitigate the effects of normal and pathological aging, offering valuable guidance for middle-aged adults and healthcare professionals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".