Needed competence for registered nurses working at a patient-centred telehealth service aimed to engage and empower people living with COPD: A five-month participatory observational study
Bibliographic record
Abstract
BACKGROUND: The global population of older aged 65 and over is increasing, which means an increase in people living with long-term health conditions and multimorbidity. Implementing new digital health technologies enables increased patient empowerment and responsibility, and the ability to respond to changes in their condition themselves, with less involvement of healthcare professionals. Important parameters need to be addressed for this digitally enabled empowerment to be successful, these include increased individual and organizational health literacy, the establishment of joint decision-making activities among patients and healthcare professionals, and efforts that target the individual's ability to manage their condition, which include education to increase skills and providing technology for self-monitoring. OBJECTIVE: To identify needed competencies of digital healthcare professionals to be able to provide the needed services to service users with chronic obstructive pulmonary disease in a 24/7 digital healthcare service. METHOD: Five registered nurses' work was observed weekly for five months. In total 13 participatory observations were conducted. Data from the observations was transcribed and analysed through inductive content analysis. RESULTS: Five main categories were identified in the analysis; 1) tasks, 2) communication, 3) the relationships between the registered nurses, 4) service users, and 5) technology. These categories contain different competencies needed for registered nurses working in a digitalized healthcare system. CONCLUSIONS: Future digital healthcare professionals will require several competencies, to be able to deliver proper care in a digital health community that goes beyond traditional healthcare competencies, including social, technological, and communication skills.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".