Development of a “Cellphone Stewardship Framework”: Legal, Regulatory, and Ethical Issues
Bibliographic record
Abstract
Introduction:Use of mobile devices within the health care sector has become commonplace in most developed countries, and increasingly common in developing countries. Such technological innovations have outpaced the necessary awareness and understanding of the spectrum of issues that ensure appropriate use of these innovations. The term “stewardship” has been defined and is applied to the appropriate care and use of cellphones by health care providers. Aim:To examine cellphone stewardship issues, and develop a simple framework by which to categorize these issues, using clinical WhatsApp® (WhatsApp Inc., Menlo Park, CA) use as the exemplar. Methods:Nine electronic databases were searched (January 2019) for articles on WhatsApp in clinical service. Inclusion criteria were article was in English, reported on WhatsApp use or potential use in clinical practice, and identified cellphone stewardship issues. Results:Of 590 articles related to WhatsApp use in clinical practice, 167 potentially addressed some form of stewardship issue. After further review of full-text articles, 13 met the inclusion criteria, addressing specific issues related to cellphone stewardship, as defined. Articles were from nine countries (six developing and seven developed economies). Cellphone stewardship issues were abstracted and categorized into legal, regulatory, and ethical aspects, leading to development of the Cellphone Stewardship Framework for Health Care Providers (CSF-HCP). Conclusion:The CSF-HCP facilitates informed and structured debate around this topic, and encourages application of the term “cellphone stewardship” to describe and encompass the diverse legal, regulatory, and ethical issues requiring debate, resolution, and routine practice to ensure appropriate use of cellphones, and other mobile devices, by health care practitioners.
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 | 0.259 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.022 | 0.017 |
| 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".