Telehealth experiences in Canadian veterans: associations, strengths and barriers to care during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Identifying barriers to care in veteran populations is critical, as veterans face increased social isolation, relationship strains and financial insecurities. For Canadian veterans experiencing barriers to accessing healthcare, telehealth may be a promising alternative with comparable effectiveness to in-person services; however, the potential benefits and limitations of telehealth require further examination to determine its long-term utility, and to inform health policy and planning. The goal of the present research was to identify predictors and barriers to telehealth usage in Canadian veterans in the context of the COVID-19 pandemic. METHODS: 12.92; 77.4% men). We assessed reported telehealth use (ie, for mental healthcare, physical healthcare), healthcare access (ie, difficulty accessing care, avoidance of care) and mental health and stress since the beginning of the COVID-19 pandemic, sociodemographic variables and open-ended responses about telehealth experiences. RESULTS: Findings suggest that sociodemographic factors and previous telehealth use were significantly associated with telehealth use during the COVID-19 pandemic. Qualitative evidence highlighted both the benefits (eg, reducing barriers of access) and drawbacks (eg, not all services can be delivered) of telehealth services. CONCLUSIONS: This paper provided a deeper understanding of Canadian veterans' experiences with accessing telehealth care during the COVID-19 pandemic. While for some, the use of telehealth mitigated perceived barriers (eg, safety concerns of leaving home), others felt that not all health services could be appropriately carried out through telehealth. Altogether, findings support the use of telehealth services in increasing care accessibility for Canadian veterans. Continued use of quality telehealth services may be a valuable form of care that extends the reach of healthcare professionals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".