“There’s an App for That”: An Interview with Dr. Jennifer Stinson, an M-Health Expert
Bibliographic record
Abstract
Increased adoption of smartphone technology by the general public has opened up an exciting new means by which healthcare professionals can interact with their patients [1]. The smartphone’s unique ability to combine mobile communication and computation offers a novel modality by which physicians can deliver healthcare interventions to their patients. Thus, it is no wonder that the use of smartphones in healthcare settings (so called “m-health”) has become the focus of widespread interest amongst healthcare professionals, with many smartphone-based medical applications already in widespread use amongst physicians and patients [2]. Leading the charge in this m-health revolution is Dr. Jennifer Stinson, a nurse clinician scientist based at The Hospital for Sick Children in Toronto, who aims to capitalize on the popularity of smartphones among adolescents [3]. Dr. Stinson is a pioneer in the field of m-health, creating one of the first electronic pain diaries using the Palm Tungsten PDA to help adolescents with juvenile idiopathic arthritis (JIA) related pain [4]. More recently, she has created the “Pain Squad” smartphone-based app, a multiple award-winning pain measurement tool for children and adolescents with cancer [5].I was able to speak with Dr. Stinson about her experience with m-health, her views about the future of m-health, and her advice for interested healthcare professionals and trainees who want to integrate mobile technology into their own patient care. The following is an edited version of that conversation.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".