A systematic review and meta‐analysis on basic psychological need satisfaction, motivation, and well‐being in later life: Contributions of self‐determination theory
Bibliographic record
Abstract
As the population ages, it is important to understand the factors that contribute to well-being in the elderly. The purpose of this study is to perform a systematic review and meta-analysis of research on well-being among elderly people conducted particularly within the framework of self-determination theory and, more precisely, to study the relationships among basic psychological need satisfaction, motivation, and well-being. Therefore, a systematic search of the literature was conducted using the databases PsycINFO, PsycARTICLES, Academic Onefile, MEDLINE, Science Direct, and ERIC to find studies published in English and French. China National Knowledge Infrastructure (CNKI) was used to find studies published in Chinese. Ultimately, 23 studies (mainly from the United States, Canada, and Europe; no studies published in China were found) in the field of aging were used for the systematic analysis, 17 of which were used for the meta-analysis. The results reported in these publications are relatively congruent with the idea that basic psychological need satisfaction and motivation (autonomous types) are positively associated with positive indicators of well-being (meaning in life, life satisfaction, positive affect, self-esteem, etc.) and negatively associated with negative indicators of well-being (depression, apathy, etc.).
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".