A Survey of Wellness Management Strategies Used by Canadian Doctors of Chiropractic
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate if Canadian doctors of chiropractic consider using wellness strategies after functional recovery in acute and chronic conditions. This study also attempted to determine if there is a difference in the use of wellness management strategies between broad and narrow scope practitioners. METHODS: Forty-one practicing, licensed chiropractors were recruited to complete an interview survey regarding 2 mock clinical case presentations. Interviews were recorded, and influential words or word pairings were identified. Investigators formulated criteria to divide practitioners into broad scope (mixers) and narrow scope (straights). Data were analyzed using Crawdad Analysis Software (version 1.2). RESULTS: All subjects indicated that they would provide information regarding public health and wellness strategies to a patient after functional resolution of the presenting chronic or acute complaints. The responses of broad scope (mixer) chiropractors appeared to be focused on the patient specifically, whereas narrow scope (straight) responses appeared to be more varied when analyzed for noun and noun-pair influence. CONCLUSION: This study of practicing, licensed Canadian chiropractors suggests that wellness strategies may be commonly considered in practice. All subjects in this study reported a number of strategies to educate patients regarding wellness after functional recovery of a complaint.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".