Community Mental Health Clinicians’ Perspectives on Telehealth During the COVID-19 Pandemic: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: In March 2020, a rapid shift to telehealth occurred in community mental health settings in response to the need for physical distancing to decrease transmission of the virus causing COVID-19. Whereas treatment delivered over telehealth was previously utilized sparingly in community settings, it quickly became the primary mode of treatment delivery for the vast majority of clinicians, many of whom had little time to prepare for this shift and limited to no experience using telehealth. Little is known about community mental health clinicians' experiences using telehealth. Although telehealth may make mental health treatment more accessible for some clients, it may create additional barriers for others given the high rates of poverty among individuals seeking treatment from community mental health centers. OBJECTIVE: We examined community mental health clinicians' perspectives on using telehealth to deliver trauma-focused cognitive behavioral therapy to youth. We sought to better understand the acceptability of using telehealth, as well as barriers and facilitators to usage. METHODS: We surveyed 45 clinicians across 15 community clinics in Philadelphia. Clinicians rated their satisfaction with telehealth using a quantitative scale and shared their perspectives on telehealth in response to open-ended questions. Therapists' responses were coded using an open-coding approach wherein coders generated domains, themes, and subthemes. RESULTS: Clinicians rated telehealth relatively positively on the quantitative survey, expressing overall satisfaction with their current use of telehealth during the pandemic, and endorsing telehealth as a helpful mode of connecting with clients. Responses to open-ended questions fell into five domains. Clinicians noted that (1) telehealth affects the content (ie, what is discussed) and process (ie, how it is discussed) of therapy; (2) telehealth alters engagement, retention, and attendance; (3) technology is a crucial component of utilizing telehealth; (4) training, resources, and support are needed to facilitate telehealth usage; and (5) the barriers, facilitators, and level of acceptability of telehealth differ across individual clinicians and clients. CONCLUSIONS: First, telehealth is likely a better fit for some clients and clinicians than others, and attention should be given to better understanding who is most likely to succeed using this modality. Second, although telehealth increased convenience and accessibility of treatment, clinicians noted that across the board, it was difficult to engage clients (eg, young clients were easily distracted), and further work is needed to identify better telehealth engagement strategies. Third, for many clients, the telehealth modality may actually create an additional barrier to care, as children from families living in poverty may not have the requisite devices or quality broadband connection to make telehealth workable. Better strategies to address disparities in access to and quality of digital technologies are needed to render telehealth an equitable option for all youth seeking mental health 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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".