Coaching to Support Mental Health Apps: Exploratory Narrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The therapeutic alliance is crucial for the success of face-to-face therapies. Little is known about how coaching functions and fosters the therapeutic alliance in asynchronous treatment modalities such as smartphone apps. OBJECTIVE: The aim of this paper was to assess how coaching functions and fosters the therapeutic alliance in asynchronous treatment modalities. METHODS: We conducted a selected review to gather preliminary data about the role of coaching in mobile technology use for mental health care. We identified 26 trials using a 2019 review by Tønning et al and a 2021 scoping review by Tokgöz et al to assess how coaching is currently being used across different studies. RESULTS: Our results showed a high level of heterogeneity as studies used varying types of coaching methods but provided little information about coaching protocols and training. Coaching was feasible by clinicians and nonclinicians, scheduled and on demand, and across all technologies ranging from phone calls to social media. CONCLUSIONS: Further research is required to better understand the effects of coaching in mobile mental health treatments, but examples offered from reviewed papers suggest several options to implement coaching today. Coaching based on replicable protocols that are verifiable for fidelity will enable the scaling of this model and a better exploration of the digital therapeutic alliance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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 it