Telehealth in the context of COVID-19: An Analysis of Men’s Usage and Perceptions in Comparison to In-Person Healthcare
Bibliographic record
Abstract
Introduction: The rise in telehealth adoption due to the emergence of COVID-19 may have had implications for men who experience barriers to accessing traditional forms of healthcare. This study sought to explore how a sample of older men interacted with telehealth during the pandemic. Method: Data sourced from a cross-sectional, population-based questionnaire (completed from October 2020 to March 2021) were used to analyze the characteristics of older men’s (a) use of telehealth services, and (b) perceptions of telehealth in comparison to in-person healthcare using Andersen’s Behavioral Model of Health Services Use. Results: Of the 731 participants (mean age = 69 years; SD = 9.6), 241 (32.9%) had used telehealth services during pandemic restrictions. Most of them who had used telehealth (63.1%; 152/241) thought it was “just as good” as in-person, 4.1% (10/241) believed it was “better,” and 25.7% (62/241) thought it was “worse.” Men with more chronic conditions were more likely to (a) have used telehealth (odds ratio [OR], 1.44 [95% CI, 1.21–1.71]) and (b) perceived telehealth as “better” or “just as good” as in-person healthcare (OR, 1.63 [95% CI, 1.17–2.29]). Men with clinically significant depressive symptoms were more likely to view telehealth as worse than in-person care (OR, 0.32 [95% CI, 0.12–0.88]). Conclusion: While telehealth is acceptable to the majority of middle-aged and older men who have used it during the pandemic, attitudes may vary according to their current health issues. Men with more chronic conditions are more likely to feel positive about telehealth, while those with clinically significant depression symptoms are more likely to view it negatively. Healthcare providers should consider men’s needs and preferences when offering telehealth services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".