Exploration of chiropractic students' motivation toward the incorporation of new evidence on chiropractic maintenance care: a mixed methods study.
Bibliographic record
Abstract
Objectives: This sequential explanatory mixed-method study aimed to explore chiropractic students' attitudes toward incorporating maintenance care (MC) focused evidence. Methods: Attitudes towards using an evidence-based clinical protocol for maintenance care (MC), the MAINTAIN instrument, were assessed via surveys, monologue responses, dialogues, and qualitative feedback. Participants from a single chiropractic educational institution completed questionnaires evaluating their perspectives on patient-centeredness, chronic pain, and evidence incorporation. Descriptive statistics summarized quantitative data, while content analysis was used for qualitative data. Results: 74.4% (n=419) of students participated, mostly male (57.5%), with an average GPA of 3.15 (out of a maximum of 4.0). Qualitative analysis identified the need to clarify MC terminology and factors motivating students to adopt new evidence, such as quality and alignment with healthcare beliefs. Conclusions: This study's findings emphasize the importance of refining healthcare training strategies, including defining terminology and addressing motivators for evidence incorporation, as evidence for MC for low back pain evolves.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".