Impact of the Covid-19 Pandemic on Medical Office Assistants Working in Family Medicine Clinics in Ontario
Bibliographic record
Abstract
Context: Medical Office Assistants (MOAs) are front line workers and the most accessible member of the team for patients seeking primary care. MOAs, also known as receptionists, clerks, secretaries and medical administrative assistants have direct contact with patients. Historically, their contributions to primary care have been unrecognized and undervalued. The COVID-19 pandemic put pressure on existing roles and systems in primary care. MOAs likely made significant contributions to organizing new processes of providing and triaging primary care during this time. Objective: To explore the experiences of MOAs working in primary care practices during the COVID-19 pandemic from the perspectives of MOAs and family physicians(FPs) who worked with MOAs during this period. Study Design and Analysis: Qualitative study using Constructivist Grounded Theory. Seventeen individual semi-structured interviews were conducted with MOAs and FPs. Setting: Province of Ontario, Canada. Population Studied: MOAs and FPs. Intervention/Instrument: N/A Outcome Measures: N/A Results: MOAs’ many responsibilities in primary care intensified during the pandemic. MOAs leveraged their healthcare system knowledge and therapeutic relationships with patients to reduce patient distress. The MOA-FP relationship was strengthened when FPs recognized MOAs’ critical role on primary care teams, expressed concern for their welfare and included MOAs in pandemic planning and educational sessions. Conclusions: The ability of MOAs to adapt to new systems and respond to high patient needs during the pandemic appeared to be positively influenced by their relationships with patients and FPs. This study addresses a significant gap in the healthcare literature concerning the important role of MOAs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".