Mechanisms for communicating within primary health care teams.
Bibliographic record
Abstract
OBJECTIVE: To explore the types of communication used within primary health care teams (PHCTs), with a particular focus on the mechanisms teams use to promote optimal clinical and administrative information sharing. DESIGN: A descriptive qualitative study. SETTING: Primary health care teams in Ontario between August 2004 and October 2005. PARTICIPANTS: Purposive sampling was used to recruit 121 members from 16 PHCTs reflecting a range of health care professionals, including family physicians, nurse practitioners, nurses, pharmacists, dietitians, social workers, office managers, health promoters, and receptionists. METHODS: Individual in-depth interviews were conducted. An iterative analysis process was used to examine the verbatim transcripts created from the interviews. Techniques of immersion and crystallization were used in the analysis. MAIN FINDINGS: Analysis of the data revealed that communication occurs through formal and informal means. Formal communication included regular team meetings with agendas and meeting minutes, memorandums, computer-assisted communication, and communication logs. Informal communication methods were open and opportunistic, reflecting the traditional hallway consultation. For patient care issues, face-to-face communication was preferred. Team member attributes facilitating communication included approachability, availability, and proximity. Finally, funding issues could be an impediment to optimal communication. CONCLUSION: Primary health care is experiencing demands for enhanced and efficient communication that optimizes team functioning and patient care. This study describes formal and informal mechanisms of communication currently used by PHCTs. Attributes that facilitate team communication, such as approachability, availability, and proximity of team members, were highlighted. New funding arrangements might alleviate concerns about remuneration for attendance at meetings.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".