Lessons Learned from Recruiting Socially Isolated Older Immigrants for a Survey-based Study in Toronto
Bibliographic record
Abstract
[para. 1]: " The COVID-19 pandemic brought to the forefront the detrimental health and social consequences of social isolation (Hosseinzadeh et al., 2022). For many older immigrant adults, these consequences are all too familiar. The loss of familiar social networks that resulted from immigrating to and settling in a new country coupled with systemic racism, language discordance, financial precarity, acculturation stress, limited mobility during winter months, costs of or lack of (accessible) transportation, and a lack of access to information and community resources, among other factors, contribute to their social isolation (Guruge et al., 2019; Sidani et al., 2022). Even though COVID-related public health restrictions have been lifted and there is return to ‘normalcy,’ older immigrant adults continue to experience social isolation. In this paper, opportunities, and challenges of reaching out to socially-isolated older immigrant adults in a research context are presented."
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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 | 0.042 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".