Chiropractic students’ characteristics influencing confidence and competence in modulating spinal manipulation force–time characteristics of specific target forces: a secondary analysis of a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Although distinct, confidence and competence play a valuable role in healthcare education. For chiropractic students, both may be important in mastering motor skills required to perform spinal manipulative therapy (SMT). However, little is known about how individual factors influence students' confidence and competence. Better understanding of these associations would enable the development of tailored training. Therefore, this study aimed to investigate associations between demographics, anthropometrics, and prior SMT experience and confidence and competence in performing SMT with specific force-time characteristics in chiropractic students. METHODS: This secondary analysis of a cross-sectional study involved 149 chiropractic students who performed SMT targeting specific peak thrust forces (200 N, 400 N, 800 N). Students were assessed for competence in force-time characteristics (preload, peak thrust force, time to peak force) using the force-sensing table technology, and self-reported their confidence in performing each characteristic. Demographics, anthropometrics, and SMT experience were collected and multivariable linear and logistic regressions were used to assess associations. RESULTS: Confidence was higher in male students, students in later years of study, and those with more SMT experience. Competence in time to peak force was higher among males and third-year students, whereas males and taller students were more likely to reach the 800 N peak thrust force. No other associations were found for competencies. CONCLUSIONS: While certain demographic and experiential factors are associated with increased confidence, these do not consistently translate to competence in SMT force-time characteristics. Targeted training approaches that account for individual student factors to better support them in developing their SMT motor skills are needed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".