Examining organizational change in primary care practices: experiences from using ethnographic methods
Bibliographic record
Abstract
BACKGROUND: Qualitative methods are an important part of the primary care researcher's toolkit providing a nuanced view of the complexity in primary care reform and delivery. Ethnographic research is a comprehensive approach to qualitative data collection, including observation, in-depth interviews and document analysis. Few studies have been published outlining methodological issues related to ethnography in this setting. OBJECTIVE: This paper examines some of the challenges of conducting an ethnographic study in primary care setting in Canada, where there recently have been major reforms to traditional methods of organizing primary care services. METHODS: This paper is based on an ethnographic study set in primary care practices in Ontario, Canada, designed to investigate changes to organizational and clinical routines in practices undergoing transition to new, interdisciplinary Family Health Teams (FHTs). The study was set in six new FHTs in Ontario. This paper is a reflexive examination of some of the challenges encountered while conducting an ethnographic study in a primary care setting. RESULTS: Our experiences in this study highlight some potential benefits of and difficulties in conducting an ethnographic study in family practice. Our study design gave us an opportunity to highlight the changes in routines within an organization in transition. A study with a clinical perspective requires training, support, a mixture of backgrounds and perspectives and ongoing communication. CONCLUSIONS: Despite some of the difficulties, the richness of this method has allowed the exploration of a number of additional research questions that emerged during data analysis.
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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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".