Factors influencing mental health providers’ intention to use telepsychotherapy in First Nations communities
Bibliographic record
Abstract
Telemental health is the use of information and communications technologies and broadband networks to deliver mental health services and support wellness. Although numerous studies have demonstrated the efficiency and utility of telemental health, certain barriers may impede its implementation, including the attitudes of mental health service providers. The current study draws on the technology acceptance model (TAM) to understand the role of mental health service providers' attitudes and perceptions of telemental health (psychotherapy delivered via videoconferencing) on their intention to use this technology with their patients. A sample of 205 broadly defined mental health service providers working on 32 First Nations reserves in the province of Quebec completed the questionnaire adapted to assess TAM for telepsychotherapy. Confirmatory factor analysis and structural equation modeling provided evidence for the factor validity and reliability of the TAM in this sample. The key predictor of the intention to use telepsychotherapy was not mental health providers' attitude toward telepsychotherapy, nor how much they expected this service to be complicated to use, but essentially how useful they expect it to be for their First Nations patients. If telemental health via videoconferencing is to be implemented in First Nations communities, it is essential to thoroughly demonstrate its utility to mental health providers. Perceived usefulness will have a positive impact on attitudes toward this technology, and perceived ease of use will positively influence perceived usefulness. Cultural issues specific to the populations receiving telemental health services may be more efficiently addressed from the angle of perceived usefulness.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".