Determinants of loss to follow-up in the Canadian Longitudinal Study on Aging: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Systematic loss to follow-up (LFU) creates selection bias and hinders generalisability in longitudinal cohort studies. Little is known about LFU risks in underserved populations including immigrants, those with depressive symptoms and language minorities. We used the Canadian Longitudinal Study on Aging (baseline 2012-2015 and 3-year follow-up 2015-2018) comprehensive and tracking cohorts to examine the association of language with LFU and its effect modification by immigrant status and depressive symptoms among participants from Quebec and those from outside Quebec. METHODS: Language was English-speaking, French-speaking and Bilingual according to the language participants' reported being able to converse in. Language minorities were French-speakers outside Quebec and English-speakers inside Quebec. LFU was withdrawal or not providing follow-up data. Logistic regression models assessed the associations of interest. RESULTS: Our cohort included 49 179 individuals (mean age 63.0, SD 10.4 years; 51.4% female). Overall, 7808 (15.9%) were immigrants and 7902 (16.1%) had depressive symptoms. Language was 4672 (9.5%) French-speaking, 33 532 (68.2%) English-speaking and 10 976 (22.3%) Bilingual. Immigration ≤20 years (OR 1.84, 95% CI 1.34 to 2.53) or arrival at age >22 years (1.32, 95% CI 1.10 to 1.58) and depressive symptoms (1.23, 95% CI 1.13 to 1.46) had higher LFU risks. Bilingual (vs French-speaking) had lower LFU risk outside (0.45, 95% CI 0.24 to 0.86) and inside Quebec (0.78, 95% CI 0.63 to 0.98). LFU risk was higher in French-speakers (vs English-speakers) outside (2.33, 95% CI 1.19 to 4.55), but not inside Quebec. Female, higher income, higher education and low nutritional risk had lower LFU risks. CONCLUSION: Speaking only French (vs Bilingual), having depressive symptoms and immigrant status increased LFU risks, with the latter not modifying the language effect.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".