Conflict on interprofessional primary health care teams – can it be resolved?
Bibliographic record
Abstract
Increasingly, primary health care teams (PHCTs) depend on the contributions of multiple professionals. However, conflict is inevitable on teams. This article examines PHCTs members' experiences with conflict and responses to conflict. This phenomenological study was conducted using in-depth interviews with 121 participants from 16 PHCTs (10 urban and 6 rural) including a wide range of health care professionals. An iterative analysis process was used to examine the verbatim transcripts. The analysis revealed three main themes: sources of team conflict; barriers to conflict resolution; and strategies for conflict resolution. Sources of team conflict included: role boundary issues; scope of practice; and accountability. Barriers to conflict resolution were: lack of time and workload; people in less powerful positions; lack of recognition or motivation to address conflict; and avoiding confrontation for fear of causing emotional discomfort. Team strategies for conflict resolution included interventions by team leaders and the development of conflict management protocols. Individual strategies included: open and direct communication; a willingness to find solutions; showing respect; and humility. Conflict is inherent in teamwork. However, understanding the potential barriers to conflict resolution can assist PHCTs in developing strategies to resolve conflict in a timely fashion.
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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 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".