Burden of administrative responsibilities in primary care
Bibliographic record
Abstract
OBJECTIVE: To describe family physicians' experiences of administrative burden in practice. DESIGN: Qualitative study using constructivist grounded theory. SETTING: Ontario. PARTICIPANTS: Family physicians. METHOD: In-depth virtual interviews with family physicians practising in Ontario who completed postgraduate training between 2017 and 2022. MAIN FINDINGS: A total of 36 family physicians were interviewed. Without external prompting, all participants raised the issue of administrative burden, offering specific contextual factors contributing to their administrative burden. These included volume of paperwork, inbox management, and lack of compensation for the hours of administrative tasks performed. In addition to these contextual factors, 2 main themes were identified: the first revealed the impact of administrative burden on both the time available for patient care and physicians' well-being. This latter issue was exacerbated by deteriorating relationships with specialist colleagues, contributing to family physicians' administrative burden and burnout. A lack of exposure to the volume of administrative duties during training added to this issue. The second theme described participants' personal strategies (eg, creating flex time, setting boundaries) and system solutions (eg, need for compensation for administrative time, funding to increase clinic staff, and interventions by regulatory bodies) to address administrative burden. CONCLUSION: Administrative burden negatively impacts physician well-being and reduces time for direct patient care. These findings highlight 2 new sources contributing to administrative burden: deteriorating relationships between family physicians and specialist colleagues and a lack of exposure to managing administrative responsibilities during medical training. Study findings provide personal strategies and system solutions to guide practitioners, policy-makers, and educators.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".