Interprofessional collaboration within integrative healthcare clinics through the lens of the relationship-centered care model
Bibliographic record
Abstract
Teamwork is a contemporary way to try to improve the healthcare system, not only for the patients but also for the practitioners involved. A new type of interprofessional working arrangement, integrative healthcare (IHC) clinics, has emerged in the last two decades. The literature on interprofessional collaboration is steadily increasing, but little is known about the collaborative organization of the biomedical and complementary and alternative medicine (CAM) practitioners that make up the teams in these clinics. The relationship-centered care model was used to guide an exploration of the interprofessional teamwork within a Canadian IHC setting. A sample of 31 IHC clinics and 228 biomedical and CAM practitioners were included. Eighty-nine questionnaires were returned from 25 clinics, representing a 62% practitioner response rate (within clinic responders). This study established that within the analytical model, practitioners behaviors and skills are the main factors associated with job satisfaction and inter-practitioner conflicts in interprofessional IHC practice. The results of the study also suggested the importance of interprofessional exposure for healthcare practitioners who are being expected to serve a clientele that is increasingly interested in being both cured and healed by the integration of biomedical and CAM paradigms and approaches.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".