Isolating together during COVID-19: Results from the Telehealth Intervention Program for older adults
Bibliographic record
Abstract
Background A pressing challenge during the COVID-19 pandemic and beyond is to provide accessible and scalable mental health support to isolated older adults in the community. The Telehealth Intervention Program for Older Adults (TIP-OA) is a large-scale, volunteer-based, friendly telephone support program designed to address this unmet need. Methods A prospective cohort study of 112 TIP-OA participants aged ≥60 years old was conducted in Quebec, Canada (October 2020–June 2021). The intervention consisted of weekly friendly phone calls from trained volunteers. The primary outcome measures included changes in scores of stress, depression, anxiety, and fear surrounding COVID-19, assessed at baseline, 4 and 8-weeks. Additional subgroup analyses were performed with participants with higher baseline scores. Results The subgroup of participants with higher baseline depression scores (PHQ9 ≥10) had significant improvements in depression scores over the 8-week period measured [mean change score = −2.27 (±4.76), 95%CI (−3.719, −0.827), p = 0.003]. Similarly, participants with higher baseline anxiety scores (GAD7 ≥10) had an improvement over the same period, which, approached significance (p = 0.06). Moreover, despite peaks in the pandemic and related stressors, our study found no significant (p ≥ 0.09) increase in stress, depression, anxiety or fear of COVID-19 scores. Discussion This scalable, volunteer-based, friendly telephone intervention program was associated with decreased scores of depression and anxiety in older adults who reported higher scores at baseline (PHQ 9 ≥10 and GAD7 ≥10).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".