Data_Sheet_3_Connecting During COVID-19: A Protocol of a Volunteer-Based Telehealth Program for Supporting Older Adults' Health.PDF
Bibliographic record
Abstract
<p>Introduction: Social-distancing due to COVID-19 has led to social isolation, stress, and mental health issues in older adults, while overwhelming healthcare systems worldwide. Telehealth involving phone calls by trained volunteers is understudied and may be a low-cost, scalable, and valuable preventive tool for mental health. In this context, from patient participatory volunteer initiatives, we have adapted and developed an innovative volunteer-based telehealth intervention program for older adults (TIP-OA).</p><p>Methods and analysis: To evaluate TIP-OA, we are conducting a mixed-methods longitudinal observational study.</p><p>Participants: TIP-OA clients are older adults (age ≥ 60) recruited in Montreal, Quebec.</p><p>Intervention: TIP-OA volunteers make weekly friendly phone calls to seniors to check in, form connections, provide information about COVID-19, and connect clients to community resources as needed.</p><p>Measurements: Perceived stress, fear surrounding COVID-19, depression, and anxiety will be assessed at baseline, and at 4- and 8-weeks. Semi-structured interviews and focus groups will be conducted to assess the experiences of clients, volunteers, and stakeholders.</p><p>Results: As of October 15th, 2020, 150 volunteers have been trained to provide TIP-OA to 305 older clients. We will consecutively select 200 clients receiving TIP-OA for quantitative data collection, plus 16 volunteers and 8 clinicians for focus groups, and 15 volunteers, 10 stakeholders, and 25 clients for semi-structured interviews.</p><p>Discussion: During COVID-19, healthcare professionals' decreased availability and increased needs related to geriatric mental health are expected. If successful and scalable, volunteer-based TIP-OA may help prevent and improve mental health concerns, improve community participation, and decrease healthcare utilization.</p><p>Clinical Trial Registration: ClinicalTrials.gov NCT04523610; https://clinicaltrials.gov/ct2/show/NCT04523610?term=NCT04523610&draw=2&rank=1</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.302 | 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 teacher head, 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".