Integrating the Voice of Healthcare Workers in Requirements Elicitation: A Balance Between Rigour and Relevance
Bibliographic record
Abstract
Peer support programs (PSPs) are a recognized intervention to help healthcare workers (HCWs) mitigate burnout. These programs are increasingly delivered using information and communication technologies. While technology-enabled PSPs bring advantages such as accessibility, challenges related to communication, perceived lack of safety, and participant engagement hinder their effectiveness. In line with a body of literature on this topic, lacking trust in technologies and other individuals in the programs are at the root of these challenges. We thus aim to understand how technologies used for enabling PSPs could be designed to enhance trust. In this paper, we propose a methodology to identify the meta-requirements and design principles that could guide the design of peer support systems (PSS) to meet this objective. We adopt a design science research (DSR) methodology, which provides principles and a process to move rigorously from guiding theories and frameworks of trust to meta-requirements and design principles. To enhance the relevance of results, we complement this methodology by integrating a prototyping approach that involves HCWs as end users. This approach allows iteratively adapting and refining the results of the research in a manner that reflects HCWs' priorities and perspectives. This approach provides valuable insights for future research on well-being and health, focusing on identifying user-centred requirements that can be generalized across a class of systems.
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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 | 0.353 | 0.461 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".