Effectiveness of Direct Observation of Procedural Skills (DOPS) for Improving the Mini-Implant Insertion Procedural Skills of Postgraduate Orthodontic Trainees
Bibliographic record
Abstract
Background: College of Physicians & Surgeons Pakistan (CPSP) has recently introduced different WBA tools in various specialties, but Directly Observed Procedural Skills (DOPS) is still required to be implemented in orthodontics which will improve learning and skills of the learner. Aim: To determine the effect of applying DOPS for improving the mini-implant insertion procedural skills of postgraduate orthodontic trainees. Methodology: This quantitative, quasi-experimental study was conducted at orthodontic department of de’Montmorency College of Dentistry (DCD), Lahore, from 1st July 2021 to 1st November 2021. Twenty trainees were selected and assessed over a period of 3 months which included 3 DOPS encounters with one-month interval between each encounter for mini-implant insertion. The trainees were assigned to faculty for each DOPS encounter by randomization. At the end, the trainees’ and the faculty’ perception regarding feasibility and acceptability of DOPS were obtained by means of structured questionnaire. The pre-DOPS and post-DOPS mean scores of all the trainees were compared using paired t-test. Results: Mean scores of all the orthodontic trainees significantly improved in the post-DOPS as compared to the pre-DOPS encounter, which may be linked to the feedback of 2nd DOPS session. As per results, the DOPS was perceived to be acceptable and feasible to both the faculty and trainees. Conclusion: DOPS is an effective tool for assessing and improving the mini-implant insertion procedural skills of postgraduate orthodontic trainees. Keywords: Direct Observation of Procedural Skills (DOPS); Mini-implants; Orthodontics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".