Leadership in Digital Health Services: Protocol for a Concept Analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Due to the rapid digitalization of health care, leadership is becoming more complex. Leadership in digital health services is a term that has been used in the literature with various meanings. Conceptualization of leadership in digital health services is needed to deliver higher quality digital health services, update existing leadership practices, and advance research. OBJECTIVE: The aim of this study is to outline a concept analysis that aims to clarify and define the concept of leadership in digital health services. METHODS: The concept analysis will be performed using the Walker and Avant model, which involves eight steps: concept selection, determination of aims, identification of uses, determination of defining attributes, construction of a model case, construction of additional cases, identification of antecedents and consequences, and definition of empirical referents. A scoping literature search will be performed following the search protocol for scoping reviews by the Joanna Briggs Institute to identify all relevant literature on leadership in digital health services. Searches will be conducted in 6 scientific databases (CINAHL, MEDLINE, Scopus, ProQuest, Web of Science, and the Finnish database Medic), and unpublished studies and gray literature will be searched using Google Scholar, EBSCO Open Dissertations, and MedNar. RESULTS: An initial limited search of MEDLINE was undertaken on October 19, 2020, resulting in 883 records. The results of the concept analysis will be submitted for publication by July 2021. CONCLUSIONS: A robust conceptualization of leadership in digital health services is needed to support research, leadership, and education. The concept analysis model of Walker and Avant will be used to meet this need. As leadership in digital health services appears to be an interprofessional and intersectoral collaboration, defining this concept may also facilitate collaboration between professionals and sectors. The concept analysis to be conducted will also expand our understanding of leadership in digital health services. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25495.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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