The use of a mobile educational tool on pressure injury education for individuals living with spinal cord injury/disease: a qualitative research study
Bibliographic record
Abstract
BACKGROUND: As many as 30-60% of individuals living with spinal cord injury/disease (SCI/D) experience at least one pressure injury (PI) in their lifetime. Best practice guidelines in SCI/D rehabilitation emphasize the importance of providing education regarding PI prevention and management for individuals living with SCI/D. Mobile educational applications can be used for PI education however there is limited research on the user-experiences of mobile educational applications about PIs for individuals living with SCI/D. OBJECTIVES: The purpose of this study was to explore the experiences of individuals living with SCI/D on the use of Pressure Ulcer Target (PUT), a mobile educational app for PI prevention and management. METHODS/OVERVIEW: Nine participants living with SCI/D used PUT over two weeks. Individual semi-structured interviews were conducted to explore the participants' perceptions regarding the utility, aesthetics and ease of use of PUT and suggested modifications. A conventional content analysis was used to identify themes and categories from the data. RESULTS: User-experiences with PUT fell into four themes: (1) Strengths and weakness; (2) Target population; (3) Key concepts and messages; and (4) Recommendations for improvement. CONCLUSIONS: PUT serves as a review of previously acquired PI knowledge and should be introduced early in rehabilitation to motivate users to prevent PIs. Future studies exploring healthcare professionals' perspectives of PUT are warranted.Implications for rehabilitationPUT aids individuals living with SCI/D in the community to review PI prevention and management strategies that they learned as inpatients.The use of pictures to deliver patient education regarding PI prevention and management through a mHealth app is recommended.PUT should be introduced early in rehabilitation to motivate users to prevent PIs.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".