Developing key performance indicators for the Canadian chiropractic profession: a modified Delphi study
Bibliographic record
Abstract
Abstract Background The purpose of this study is to develop a list of performance indicators to assess the status of the chiropractic profession in Canada. Method We conducted a 4-round modified Delphi technique (March 2018–January 2020) to reach consensus among experts and stakeholders on key status indicators for the chiropractic profession using online questionnaires. During the first round, experts suggested indicators for preidentified themes. Through the following two rounds, the importance and feasibility of each indicator was rated on an 11-point Likert scale, and their related potential sources of data identified. In the final round, provincial stakeholders were recruited to rate the importance of the indicators within the 90th percentile and identified those most important to their organisation. Results The first round generated 307 preliminary indicators of which 42 were selected for the remaining rounds, and eleven were preferentially selected by most of the provincial stakeholders. Experts agreed the feasibility of all indicators was high, and that data could be collected through a combination of data obtained from professional liability insurance records and survey(s) of the general population, patients, and chiropractors. Conclusions A set of performance indicators to assess the status of the Canadian chiropractic profession emerged from a scientific and stakeholder consensus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".