Impact of the COVID-19 pandemic on medical office assistants (MOAs) working in primary care: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Medical office assistants (MOAs), also known as receptionists and clerks, are frontline workers and the most accessible member of the primary care team. Historically, their contributions to primary care have been unrecognised and undervalued. The COVID-19 pandemic put pressure on existing roles and systems in primary care: how MOAs adapted is unknown. AIM: To explore the experiences of MOAs working in primary care during the COVID-19 pandemic from the perspectives of MOAs and family physicians (FPs) who worked with MOAs during this period. DESIGN & SETTING: A qualitative study, using constructivist grounded theory (CGT), was conducted in Ontario, Canada. METHOD: Seventeen participants were recruited through professional contacts of the research team. Individual semi-structured interviews were undertaken with MOAs and FPs across the province. 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. Unfortunately, MOAs experienced more frustration, and in some cases, abuse from patients. MOAs' ability to adapt to new systems and respond to high patient needs seemed to be positively influenced by their relationships with patients and FPs. FPs showed support for MOA welfare and recognised their critical role on primary care teams. CONCLUSION: MOAs made considerable contributions to primary care during the COVID-19 pandemic. This study suggests MOAs have greater capacity than previously recognised, which has important implications for planning in an era of under-resourced health care.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".