“How to Save a Life”: A Premortem for Pediatric Digital Health
Bibliographic record
Abstract
Objective: Digital health tools (e.g., apps, text messaging, telehealth, and social media) have been employed with pediatric populations for approximately two decades. As such, digital health is a scientifically mature field, ready for critical evaluation of the challenges and opportunities of using technology to equitably improve child health and health care. This commentary describes the application of a premortem exercise to forecast failures (i.e., “causes of death”) of pediatric digital health and strategies to prevent them. Method: With a convenience sample of trainees and early-, mid-, and later-career pediatric psychology professionals interested in digital health ( n = 14), we conducted a one-time, premortem exercise (i.e., a collaborative discussion to envision a failure and work backward) to brainstorm potential threats and opportunities for advancing pediatric digital health. Results: We came to consensus on five core themes related to digital health threats and opportunities: (a) We became stuck in proving efficacy and did not make an impact; (b) We assumed we knew more than youth and their families; (c) We replicated, exacerbated, or created new disparities compared to in-person care; (d) We underestimated the importance of a human touch; and (e) We did not know how to advocate for digital health as a clinical transformation. Conclusion: This commentary serves as a discussion point for identifying the strategic priorities of today (e.g., designing tools collaboratively with youth, planning for clinical implementation from inception) that can advance the development and impact of digital health tools and services for pediatric populations. Implications for Impact Statement With input from pediatric psychologists who participated in a one-time “premortem” exercise (i.e., a collaborative discussion to envision the failure of digital health and work backward), this commentary reflects on today’s challenges and recommendations for promoting youth health via digital health approaches (e.g., apps).
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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 | 0.036 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.019 | 0.038 |
| Insufficient payload (model declined to judge) | 0.003 | 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".