Combining Action Research and the Teach-Back Method to Improve Perioperative Care in Hair Transplantation
Bibliographic record
Abstract
BACKGROUND: This study aimed to optimize the perioperative clinical care pathway for patients undergoing hair transplantation by integrating action research and the teach-back method, and to validate its clinical application. METHODS: A total of 116 patients undergoing hair transplantation were divided into control and experimental groups based on time periods. The control group received conventional care; the experimental group received action research combined with the teach-back method for training and assessment. After 2 iterative cycles of problem identification, planning, action, reflection, and implementation, the care pathway was refined. Outcomes measured included hair follicle survival rate, complication incidence, secondary transplantation rates within 12 months, postoperative self-management, and satisfaction. RESULTS: The experimental group had higher follicle survival rates ( Z = 8.788, P = 0.001), lower complication incidence (χ ² = 3.940, P = 0.047), improved postoperative self-management ( Z = 3.426, P = 0.001), and greater satisfaction (χ² = 4.245, P = 0.039). There was no significant difference between the 2 groups in the occurrence of secondary hair transplantation within 12 months after surgery ( P = 0.618). CONCLUSIONS: By combining action research with the teach-back method, the authors significantly optimized perioperative care, achieving notable improvements in the survival rate of hair follicle units after surgery, a reduction in the incidence of complications, and enhancements in both postoperative self-management capabilities and patient satisfaction levels. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".