Education of parents in Pavlik harness application for developmental dysplasia of the hip using a validated simulated learning module
Bibliographic record
Abstract
BACKGROUND: The Pavlik harness is the most common initial treatment for developmental dysplasia of the hip worldwide. During treatment, parents are required to re-apply the harness at home. Teaching parents how to apply the harness is therefore paramount to success. While simulated learning for medical training is commonplace, it has not yet been trialed in teaching parents how to apply a Pavlik harness. METHODS: A group of parents underwent a simulated learning module for Pavlik harness application. Parents were evaluated pre- and post-exposure and at one month after testing. A validated objective structured assessment of technical skill (OSATS) and a global rating scale (GRS) specific to Pavlik harness application were used for evaluation. A control group of parents was also tested at both time points. A clinical expert group was used to determine competency. ANOVA and t tests were used to assess differences between groups and over time. RESULTS: Parent scores on the OSATS improved to the level of expert clinicians both immediately post-intervention and at retention testing. However, on the GRS, only half were considered competent due to their inability to achieve the required hip positions. The control group did not improve nor were they considered competent. CONCLUSIONS: The use of a simulated learning module improves both the confidence and skill level of parents in the application of the Pavlik harness. However, the challenges parents face in understanding the more detailed subtleties of medical care suggest that they still require an appropriate level of supervision by clinicians to ensure effective treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".