Applying change management metaphors to a national e-Health strategy
Bibliographic record
Abstract
Recent attempts at a collective understanding of how to develop an e-Health strategy have addressed the individual organisation, collection of organisations, and national levels. At the national level the World Health Organisation's National eHealth Strategy Toolkit serves as an exemplar that consolidates knowledge in this area, guides practical implementations, and identifies areas for future research. A key implication of this toolkit is the considerable number of organisational changes required to successfully apply their ideas in practice. This study looks critically at the confluence of change management and e-Health strategy using metaphors that underpin established models of change management. Several of Morgan's organisational metaphors are presented (highlighting varied beliefs and assumptions regarding how change is enacted, who is responsible for the change, and guiding principles for that change), and used to provide a framework. Attention is then directed to several prominent models of change management that exemplify one or more of these metaphors, and these theoretical insights are applied to evaluate the World Health Organisation's National eHealth Strategy Toolkit. The paper presents areas for consideration when using the WHO/ITU toolkit, and suggestions on how to improve its use in practice. The goal is to seek insight regarding the optimal sequence of steps needed to ensure successful implementation and integration of e-health into health systems using change management models. No single model, toolkit, or guideline will offer all the needed answers, but clarity around the underlying metaphors informing the change management models being used provides valuable insight so potentially challenging areas can be avoided or mitigated.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".