Nurses’ Practical Wisdom for the Support of Dementia Patients Among Hospital Outpatients
Bibliographic record
Abstract
PURPOSE: To establish and understand nurses’ practical wisdom and interventions of support for dementia and possible dementia patients at hospital outpatient wards.METHODS: A qualitative design was used to collect data through semi-structured focus group interviews. The participants were 13 female nurses working at hospital outpatient wards. Data were analyzed using the KJ Method.RESULTS: Seven themes symbolizing the properties of the final label were extracted as follows: ‘Observation of patients with focused awareness, and are continuously engaged with their patients’, ‘Approach to the problems of the patients, and sensitively work to understand the worries of patients based on past cases of problems’, ‘Looking out for simple ways patients can look after themselves, implicitly and thoroughly, making the best use of the ways that patients are familiar with and which they are able to understand’, ‘Preparations for scheduled consultations by developing a network to assist with problem prevention and recording episodes about problems involving the patients’, ‘Requests for cooperation to continue treatment by choosing intermediaries/resources appropriately as based on the importance of the medical treatment’, ‘Responses that do not conflict with the feelings of the family by considering the possible reluctance of accepting that a family member has dementia’, and ‘Attitude not to blame matters on dementia by reflecting on how the environment and care ought to be’.CONCLUSION: Nurses’ practical wisdom is a type of support provided for patients in a natural manner without being noticed as special or particular by the patients.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".