Chiropractic student diagnosis and management of headache disorders: A survey examining self-perceived preparedness and clinical proficiency
Bibliographic record
Abstract
OBJECTIVE: To explore the self-perceived preparedness and clinical proficiency in headache diagnosis and management of Australian chiropractic students in senior years of study. METHODS: Australian chiropractic students in the 4th (n = 134) and 5th year (n = 122) of 2 chiropractic university programs were invited to participate in an online cross-sectional survey. Descriptive analyses were conducted for all variables. Post hoc analyses were performed using simple linear regression to evaluate the relationship between self-perceived preparedness and correctness of headache diagnosis and management scores. RESULTS: Australian chiropractic students in senior years demonstrated moderate overall levels of self-perceived preparedness and proficiency in their ability to diagnose and manage headache disorders. Final-year students had a slightly higher self-perceived preparedness and proficiency in headache diagnosis and management compared to those students in the 4th year of study. There was no relationship between self-perceived preparedness and correctness of headache diagnosis and management for either 4th- or 5th-year chiropractic students. CONCLUSION: Our findings suggest that there may be gaps in graduate chiropractic student confidence and proficiency in headache diagnosis and management. These findings call for further research to explore graduate chiropractic student preparedness and proficiency in the diagnosis and management of headache disorders.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".