Exploration of how primary care models influence job satisfaction among primary care providers during the COVID-19 pandemic in New Brunswick: a descriptive and comparative study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has highlighted human resource gaps and physician shortages in healthcare systems in New Brunswick (NB), as evidenced by multiple healthcare service interruptions. In addition, the New Brunswick Health Council gathered data from citizens on the type of primary care models (i.e. physicians in solo practice, physicians in collaborative practice, and collaborative practice with physicians and nurse practitioners) they use as their usual place of care. To add to their survey's findings, our study aims to see how these different primary care models were associated with job satisfaction as reported by primary care providers. METHODS: In total, 120 primary care providers responded to an online survey about their primary care models and job satisfaction levels. We used IBM's "SPSS Statistics" software to run Chi-square and Fisher's exact tests to compare job satisfaction levels between variable groups to determine if there were statistically significant variations. RESULTS: Overall, 77% of participants declared being satisfied at work. The reported job satisfaction levels did not appear to be influenced by the primary care model. Participants reported similar job satisfaction levels regardless of if they practiced alone or in collaboration. Although 50% of primary care providers reported having symptoms of burnout and experienced a decline in job satisfaction during the COVID-19 pandemic, the primary care model was not associated with these experiences. Therefore, participants who reported burnout or a decline in job satisfaction were similar in all primary care models. Our study's results suggest that the autonomy to choose a preferred model was important, since 45.8% of participants reported choosing their primary care models, based on preference. Proximity to family and friends and balancing work and family emerged as critical factors that influence choosing a job and staying in that job. CONCLUSION: Primary care providers' staffing recruitment and retention strategies should include the factors reported as determinants in our study. Primary care models do not appear to influence job satisfaction levels, although having the autonomy to choose a preferred model was reported as highly important. Consequently, it may be counterproductive to impose specific primary care models if one aims to prioritize primary care providers' job satisfaction and wellness.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".