Using a typology to understand and address primary care administrative workload in Atlantic Canada
Bibliographic record
Abstract
Context Administrative activities, including work related to caring for individual patients and clinic administration, may play a substantial role in understanding changes to primary care workload. Objective The objective of the qualitative component of this mixed methods study was to conduct interviews with family physicians, nurse practitioners, and administrative team members providing primary care: i) to describe their current experiences of administrative workload, ii) to understand how administrative workload has changed over time, and iii) to explore strategies that might be utilized to streamline processes and reduce the volume of administrative work. Study Design & Analysis We used a screening questionnaire to purposively select interview participants. Interviews were approximately one hour in duration and were conducted via Zoom. We followed Braun and Clarke’s approach to reflective thematic analysis, which fit well with our critical qualitative approach and relativist epistemology. Setting and Population Studied Interviewees were primary care providers and administrative staff representing a range of payment models, a variety of clinic models, from both urban and rural locations in Nova Scotia and New Brunswick. Intervention Thirty-six (36) interviews were conducted by one qualitative researcher. Qualitative interpretation and analysis involved representatives from each stakeholder group. Results/Findings Information management is central to health care delivery, but often not valued or actively supported. Within primary care most administrative work requires both information management and clinical judgment. Therefore, we developed a typology as part of the analysis. Participants recommended electronic medical record connectivity with other parts of the health system, pre-population of information on forms from patient charts, changes to insurance and disability forms, re-distribution of administrative tasks, assistance with overhead expenses, improved training for administrative staff, development of competencies and guidelines for clinic operations, and other actions. Conclusion Identifying practical strategies to make information management more efficient can support innovative healthcare models, improve patient care, and improve the wellbeing of primary care providers.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".