Training the Digital Clinician by Evaluating Health Education and Curriculum Integration New Zealand Psychology and Psychiatry Programs: Mixed Methods Study
Bibliographic record
Abstract
Background: The importance of digital health education is widely recognized; however, structural and knowledge deficits hinder its effective integration into training and on-the-job upskilling programs. Tackling these challenges will equip clinicians to navigate the fast-evolving digital mental health landscape confidently. Objective: This study aims to investigate the prevalence of digital health education and training needs for New Zealand mental health clinicians and trainees, including how psychology and psychiatry teaching programs are including eHealth and digital mental health tools in their curriculums. Methods: A mixed method study was conducted between August 2021 and February 2022: (1) a survey of mental health clinicians and trainees investigating existing and desired training in digital mental health tools, (2) follow-up in-depth one-on-one interviews with a subsample of survey participants, and (3) in-depth one-on-one interviews with educators (program or curriculum coordinators) within psychology and psychiatry training programs. Results: The study comprised a survey of 118 clinicians, follow-up interviews with 17 clinicians, and interviews with 4 program directors of relevant training programs. The survey results revealed that 75% (n=88) of the clinicians had not received formal digital health training, yet 69% (n=81) had engaged in self-directed learning. Interest in further training was strong, with 83% (n=98) expressing moderate-to-high interest. Two key themes emerged from the clinician interviews: (1) openness to upskilling, reflecting a willingness to learn, and (2) barriers of time and leadership, highlighting challenges in accessing training due to workloads and limited institutional support. From the program director interviews, three themes were identified: (1) curriculum overload, reflecting difficulties incorporating new content into already crowded programs; (2) uncertainty and inconsistency, with educators unsure about the scope and delivery of digital mental health education; and (3) growth and future potential, highlighting optimism about integrating digital health training into curricula. Conclusions: The findings reveal a pressing gap in formal digital health training for clinicians despite widespread interest and enthusiasm for upskilling. Key barriers-time constraints, limited institutional leadership, and a lack of educator expertise-are slowing progress.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".