Role construction and boundaries in interprofessional primary health care teams: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The move towards enhancing teamwork and interprofessional collaboration in health care raises issues regarding the management of professional boundaries and the relationship among health care providers. This qualitative study explores how roles are constructed within interprofessional health care teams. It focuses on elucidating the different types of role boundaries, the influences on role construction and the implications for professionals and patients. METHODS: A comparative case study was conducted to examine the dynamics of role construction on two interprofessional primary health care teams. The data collection included interviews and non-participant observation of team meetings. Thematic content analysis was used to code and analyze the data and a conceptual model was developed to represent the emergent findings. RESULTS: The findings indicate that role boundaries can be organized around interprofessional interactions (giving rise to autonomous or collaborative roles) as well as the distribution of tasks (giving rise to interchangeable or differentiated roles). Different influences on role construction were identified. They are categorized as structural (characteristics of the workplace), interpersonal (dynamics between team members such as trust and leadership) and individual dynamics (personal attributes). The implications of role construction were found to include professional satisfaction and more favourable wait times for patients. A model that integrates these different elements was developed. CONCLUSIONS: Based on the results of this study, we argue that autonomy may be an important element of interprofessional team functioning. Counter-intuitive as this may sound, we found that empowering team members to develop autonomy can enhance collaborative interactions. We also argue that while more interchangeable roles could help to lessen the workloads of team members, they could also increase the potential for power struggles because the roles of various professions would become less differentiated. We consider the conceptual and practical implications of our findings and we address the transferability of our model to other interprofessional teams.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".