The Role of Human and Organizational Factors in the Pursuit of One Digital Health
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
OBJECTIVE: This paper surveys a subset of the 2022 human and organizational factor (HOF) literature to provide guidance on building a One Digital Health ecosystem. METHODS: We searched a subset of journals in PubMed/Medline for studies with "human factors" or "organization" in the title or abstract. Papers published in 2022 were eligible for inclusion in the survey. Selected papers were categorized into structural and behavioural aspects to understand digital health enabled interactions across micro, meso, and macro systems. RESULTS: Our survey of the 2022 HOF literature showed that while we continue to make meaningful progress at digital health enabled interactions across systems levels, there are still challenges that must be overcome. For example, we must continue to grow the breadth of HOF research beyond individual users and systems to assist with the scale up of digital health systems across and beyond organizations. We summarize the findings by providing five HOF considerations to help build a One Digital Health ecosystem. CONCLUSION: One Digital Health challenges us to improve coordination, communication, and collaboration between the health, environmental and veterinary sectors. Doing so requires us to develop both the structural and behavioural capacity of digital health systems at the organizational level and beyond so that we can develop more robust and integrated systems across health, environmental and veterinary sectors. The HOF community has much to offer and must play a leading role in designing a One Digital Health ecosystem.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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