An online survey of chiropractors' opinions of continuing education
Bibliographic record
Abstract
BACKGROUND: Continuing Education (CE) for chiropractors is mandatory for licensure in most North American jurisdictions. Numerous chiropractic colleges have begun collaborating with universities to offer master's degree programs. Distance education master's degree programs may be desirable to allow full-time practicing doctors to further their post-graduate education. The present survey sought to answer three questions. First, what is the level of satisfaction of chiropractors with their continuing education? Second, what is the level of interest of chiropractors in online master's degree programs? Lastly, what is the response rate of chiropractors to an online survey? METHODS: An online survey consisting of 22 multiple choice questions was e-mailed to 1000 chiropractors randomly selected from the mailing list of an online chiropractic newsletter. Upon completion of the questionnaire, participants' answers were saved on a secure site. Data analysis included evaluation of the demographic characteristics of the respondents, their opinions of and patterns of taking CE including online education, preferred learning formats, and their interest in proposed online master's degree programs. A survey response rate was determined. RESULTS: Nearly 86% of respondents felt their previously completed CE courses were either somewhat or extremely satisfactory. Over ninety percent of respondents who had completed online or distance CE courses found them to be somewhat or extremely satisfactory. Almost half the respondents indicated that they most preferred online distance learning, while 34.08% most preferred face-to-face interaction. Fifty-three percent of respondents indicated an interest in starting a master's degree program; however 70.46% of respondents were interested in an online master's degree program that would offer CE credit. A response rate of 35.8% was obtained. CONCLUSION: Satisfaction among chiropractors with CE programs is high. The notion of completing a part-time online master's degree (or online combined with face-to-face interaction) appears to be popular among respondents, with a M.Sc. in Chiropractic Sciences being the most popular of those mentioned. Online surveys are a viable method of obtaining opinion in a cost and time efficient manner; there are some sources of bias involved in this type of research, and numerous steps need to be taken to obtain a suitable response rate.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.006 | 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".