Interprofessional collaboration and turf wars how prevalent are hidden attitudes?
Bibliographic record
Abstract
PURPOSE: Interprofessional collaboration in health care is believed to enhance patient outcomes. However, where professions have overlapping scopes of practice (eg, chiropractors and physical therapists), "turf wars" can hinder effective collaboration. Deep-rooted beliefs, identified as implicit attitudes, provide a potential explanation. Even with positive explicit attitudes toward a social group, negative stereotypes may be influential. Previous studies on interprofessional attitudes have mostly used qualitative research methodologies. This study used quantitative methods to evaluate explicit and implicit attitudes of physical therapy students toward chiropractic. METHODS: A paper-and-pencil instrument was developed and administered to 49 individuals (students and faculty) associated with a Canadian University master's entry-level physical therapy program after approval by the Research Ethics Board. The instrument evaluated explicit and implicit attitudes toward the chiropractic profession. Implicit attitudes were determined by comparing response times of chiropractic paired with positive versus negative descriptors. RESULTS: Mean time to complete a word association task was significantly longer (t = 4.75, p =.00) when chiropractic was associated with positive rather than negative words. Explicit and implicit attitudes were not correlated (r = 0.13, p =.38). CONCLUSIONS: While little explicit bias existed, individuals associated with a master's entry-level physical therapy program appeared to have a significant negative implicit bias toward chiropractic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".