Uncovering key determinants of well-being among older Canadian retirees in New Brunswick: Protocol for a mixed-methods study
Bibliographic record
Abstract
AIMS: To uncover the key determinants of physical, mental, social, financial, and spiritual well-being among older Canadian retirees living in New Brunswick and explore how individual, community, organizational, and societal factors interact to shape healthier, more fulfilling aging. DESIGN: Explanatory sequential mixed-methods study guided by the Socio-Ecological Model. METHODS: A cross-sectional survey will be conducted with 600 retirees aged 65 and older using the WISE Scale, a multidimensional measure of well-being. Surveys will be offered online and in person with accessibility supports. Data will be analyzed in SPSS using descriptive statistics and multiple regression. A purposive subsample of 15-25 participants will be invited for semi-structured interviews to enrich understanding of survey findings. Interviews will be thematically analyzed in NVivo, following the four pillars of trustworthiness. Triangulation will integrate quantitative and qualitative findings. DISCUSSION: The study will generate detailed insights into how multiple layers of influence affect older adults' well-being in retirement, addressing critical gaps in research, policy, and practice. Findings will inform tailored community programs, effective strategies for healthcare providers, equitable policies, and age-friendly supports that promote holistic well-being for older retirees in New Brunswick, particularly among English-speaking populations. By identifying specific factors that enhance or hinder well-being, this research will support more responsive and inclusive strategies for healthy aging.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".