Evaluation of Electronic Mental Health Implementation in Northern Territory Services Using the Integrated “Promoting Action on Research Implementation in Health Services” Framework: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Electronic mental health is a promising strategy to bridge the treatment gap in mental health care. Training workshops have been delivered to service providers working with Aboriginal and Torres Strait Islander people at a primary health care level to raise awareness and knowledge of electronic mental health approaches. OBJECTIVE: This study aimed to understand service providers' perspectives and experiences of electronic mental health adoption. More specifically, it aimed to use the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to further identify and understand how different factors facilitate or impede electronic mental health uptake within primary health care settings providing services to Aboriginal and Torres Strait Islander people. METHODS: Qualitative interviews were conducted with 57 service providers working with Aboriginal and Torres Strait Islander people, who had undergone electronic mental health training workshops. RESULTS: Several factors related to innovation (electronic mental health approach), recipients (service providers as an individual and as a team), and context (local, organizational, and external contexts) were found to influence electronic mental health uptake. Particularly, organizational readiness, in terms of information technology resources and infrastructure, policies, workforce and culture, and processes to mandate electronic mental health use, were found to be significant impediments to electronic mental health utilization. These findings led to the development of a three-phase implementation strategy that aims to enhance electronic mental health adoption by addressing organizational readiness before and post electronic mental health training. CONCLUSIONS: The i-PARIHS provides a useful determinant framework that deepens our understanding of how different factors impede or facilitate electronic mental health adoption in this setting. This insight was used to develop a practical and comprehensive implementation strategy to enhance the utilization of electronic mental health approaches within primary health care settings, involving three phases: pretraining consultations, training workshops, and post-training follow-up support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".