Who, When, How – Guiding the active involvement of stakeholders in eHealth Action Research
Bibliographic record
Abstract
Action Research (AR) is a form of iterative, collaborative research that takes place in practice and actively involves stakeholders as co-researchers. It is increasingly used in the field of eHealth, which focuses on the development and implementation of technology in the healthcare context. AR holds many benefits for eHealth research, as it can ensure a better fit between technology and practice. However, conducting AR is complex and time-consuming, especially in the healthcare context where stakeholders already experience high workload. This thesis therefore looks at how those conducting eHealth projects can be guided in doing AR. The state of the art is described first, through a review of eHealth AR literature. This is followed by chapters on the different roles and types of involvement in eHealth AR, like so called ‘champions’ of a research project, or looking at how outsiders can be involved in a project spontaneously. Then, several chapters are dedicated to how different stakeholders interact and communicate with each other within an eHealth AR project. This for example includes alignment of interests between stakeholders, and a tool for collaborative reflection. All chapters provide recommendations that others can implement into their practice. These recommendations are bundled in the last part, and combined with literature to develop a framework for active stakeholder involvement in eHealth AR. The framework was evaluated with experts and action researchers from the field, and updated based on their input. The thesis ends with a reflection on the role of meta-researcher which can allow a more methodological perspective on doing eHealth AR research, rather than just reporting outcomes.
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 | 0.118 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.012 |
| 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".