Developing Disposable Hair Washing Pad for Bedridden Patients Using Mixed Methods Research
Bibliographic record
Abstract
In Japan, diapers are often used to wash bedridden patients’ hair by placing the diaper under the patient’s head for ease of use and efficiency. However, using diapers for the head is an ethical problem as diapers are originally used for elimination care. Developing better equipment to wash hair is necessary to comfort patients and reduce nurses’ workload. Our industry-academia-clinical collaboration team developed a disposable absorbent pad particularly for washing patients’ hair in bed. This study aimed to evaluate this pad developed for bedridden patients. The trial was conducted in five departments at a university hospital between May and August 2016. The post-trial cross-sectional survey for nurses contained demographic data, evaluation of the pad with rating score as quantitative data, free comments as qualitative data, and comparison with diaper used experience. As this study involved development of an equipment and thus to facilitate data triangulation, mixed methods were used. The results revealed that 36 nurses participated (90% response rate). Most were in their 20s (69%). The overall evaluation was “good” (91%). Good water absorption, no water leakage, and easy usage were reported. In a comparison with diapers using experience, the majority preferred the developed pad (81%). Better structure, usage, and resolution of ethical issues were also confirmed in a comparison with diapers. The developed disposable hair washing pad is an efficient tool for nurses to wash bedridden patients’ hair. As it is disposable, infections are also controlled well. Further manufacturing aspects need to be considered for mega production.
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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 | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".