Global Implications From the Rise and Recession of Telehealth in Aotearoa New Zealand Mental Health Services During the COVID-19 Pandemic: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic accelerated the adoption of telehealth services for remote mental health care provision. Although studies indicate that telehealth can enhance the efficiency of service delivery and might be favored or even preferred by certain clients, its use varied after the pandemic. Once the pandemic-related restrictions eased, some regions curtailed their telehealth offerings, whereas others sustained them. Understanding the factors that influenced these decisions can offer valuable insights for evidence-based decision-making concerning the future of telehealth in mental health services. OBJECTIVE: This study explored the factors associated with the uptake of and retreat from telehealth across a multiregional outpatient mental health service in Aotearoa New Zealand. We aimed to contribute to the understanding of the factors influencing clinicians' use of telehealth services to inform policy and practice. METHODS: Applying an interpretive description methodology, this sequential mixed methods study involved semistructured interviews with 33 mental health clinicians, followed by a time-series analysis of population-level quantitative data on clinician appointment activities before and throughout the COVID-19 pandemic. The interviews were thematically analyzed, and select themes were reframed for quantitative testing. The time-series analysis was conducted using administrative data to explore the extent to which these data supported the themes. In total, 4,117,035 observations were analyzed between September 2, 2019, and August 1, 2022. The findings were then synthesized through the rereview of qualitative themes. RESULTS: The rise and recession of telehealth in the study regions were related to 3 overarching themes: clinician preparedness and role suitability, population determinants, and service capability. Participants spoke about the importance of familiarity and training but noted differences between specialist roles. Quantitative data further suggested differences based on the form of telehealth services offered (eg, audiovisual or telephone). In addition, differences were noted based on age, gender, and ethnicity; however, clinicians recognized that effective telehealth use enabled clinicians' flexibility and client choice. In turn, clinicians spoke about system factors such as telehealth usability and digital exclusion that underpinned the daily functionality of telehealth. CONCLUSIONS: For telehealth services to thrive when they are not required by circumstances such as pandemic, investment is needed in telehealth training for clinicians, digital infrastructure, and resources for mental health teams. The strength of this study lies in its use of population-level data and consideration of a telehealth service operating across a range of teams. In turn, these findings reflect the voice of a variety of mental health clinicians, including teams operating from within specific cultural perspectives.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".