No place like home: Exploring social belonging for older immigrant Muslim women
Bibliographic record
Abstract
Older adults who lack a secure sense of social belonging may experience loneliness, isolation, and feelings of being ostracized in their communities. However, little attention has been paid to the experiences of immigrant Muslim older (IMO) women and their sense of belongingness in the literature. This qualitative descriptive photo-elicitation study aimed to address this gap by exploring how immigrant Muslim older women in Edmonton, Alberta cultivated social belonging. To guide the coding and conceptualization of belongingness, an integrative framework on belongingness was utilized. The research project, of which this thesis is a part of, focused on social connectedness of IMO women. For this thesis, 14 participants were selected and thematic analysis was conducted on the transcripts and images captured during the study. The findings suggest that a sense of belonging is influenced by feelings of loneliness and loss, as well as opportunities for community engagement, and social competencies related to maintaining family relationships. Additionally, the findings indicate the importance of IMO women’s perceptions and reflections on aging experiences in shaping a sense of belonging for IMO women. These findings not only provide insight into the intricate and ever-changing nature of belongingness but also emphasize the need for structural support to benefit both IMO women and the communities they reside in.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".