Informal Support Networks of Low-Income Senior Women Living Alone: Evidence from Fort St. John, BC
Bibliographic record
Abstract
Within the context of an aging Canadian rural and small-town landscape, there is a growing trend of low-income senior women living alone. While there is a perception that rural seniors have well-developed social networks to meet their daily needs, some research suggests that economic and social restructuring processes have impacted the stability of seniors' support networks in small places. While much of the research on seniors' informal networks focuses upon small towns in decline, booming resource economies can also produce challenges for low-income senior women living alone due to both a higher cost of living and the retrenchment of government and service supports. Under such circumstances, an absence of informal supports can impact seniors' health and quality of life and may lead to premature institutionalization. Drawing upon a household survey in Fort St. John, British Columbia, we explore informal supports used by low-income senior women living alone in this different context of the Canadian landscape. Our findings indicate that these women not only have a support network that is comparable to other groups, but that they are also more likely to draw upon such supports to meet their independent-living needs. These women rely heavily on family support, however, and greater efforts are needed to diversify both their formal and informal sources of support as small family networks can quickly become overwhelmed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".