The Essential Network (TEN): Protocol for an Implementation Study of a Digital-First Mental Health Solution for Australian Health Care Workers During COVID-19
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has placed health care workers (HCWs) under severe stress, compounded by barriers to seeking mental health support among HCWs. The Essential Network (TEN) is a blend of digital and person-to-person (blended care) mental health support services for HCWs, funded by the Australian Federal Department of Health as part of their national COVID-19 response strategy. TEN is designed as both a preventative measure and treatment for common mental health problems faced by HCWs. New blended services need to demonstrate improvements in mental health symptoms and test acceptability in their target audience, as well as review implementation strategies to improve engagement. OBJECTIVE: The primary objective of this implementation study is to design and test an implementation strategy to improve uptake of TEN. The secondary objectives are examining the acceptability of TEN among HCWs, changes in mental health outcomes associated with the use of TEN, and reductions in mental health stigma among HCWs following the use of TEN. METHODS: The implementation study contains 3 components: (1) a consultation study with up to 39 stakeholders or researchers with implementation experience to design an implementation strategy, (1) a longitudinal observational study of at least 105 HCWs to examine the acceptability of TEN and the effectiveness of TEN at 1 and 6 months in improving mental health (as assessed by the Distress Questionnaire [DQ-5], Patient Health Questionnaire [PHQ-9], Generalized Anxiety Disorder [GAD-7], Oldenburg Burnout Inventory [OBI-16], and Work and Social Adjustment Scale [WSAS]) and reducing mental health stigma (the Endorsed and Anticipated Stigma Inventory [EASI]), and (3) an implementation study where TEN service uptake analytics will be examined for 3 months before and after the introduction of the implementation strategy. RESULTS: The implementation strategy, designed with input from the consultation and observational studies, is expected to lead to an increased number of unique visits to the TEN website in the 3 months following the introduction of the implementation strategy. The observational study is expected to observe high service acceptability. Moderate improvements to general mental health (DQ-5, WSAS) and a reduction in workplace- and treatment-related mental health stigma (EASI) between the baseline and 1-month time points are expected. CONCLUSIONS: TEN is a first-of-a-kind blended mental health service available to Australian HCWs. The results of this project have the potential to inform the implementation and development of blended care mental health services, as well as how such services can be effectively implemented during a crisis. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34601.
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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 | 0.080 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.114 | 0.021 |
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".