Shifting Professional Identity Among Indonesian Medical Practitioners During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has had a significant impact on medical practitioners' professional identities due to its novelty and intensity. Using constructivist grounded theory, we investigated how the COVID-19 pandemic shifted individuals' identities as medical practitioners in Indonesia, where the pandemic caused high death rates among healthcare workers, particularly medical practitioners. By interviewing 24 medical practitioners and analyzing relevant documents and reports, we developed a grounded theory of professional identity shifts. We found two patterns: (1) identity growth, in which the medical practitioners thrive and claimed stronger professional identities, and (2) psychological and moral distress leading to attrition, facilitated adaptation, or professional identity collapse. We also found several primary protective factors including religious beliefs, good leadership, team cohesion, healthy work boundaries, connection to significant others, and public acknowledgment. Without adequate protective factors, medical practitioners experienced difficulties redefining their professional identities. To cope with the situation, they focused on different identities, took some time off, or sought mental health support, resulting in facilitated adaptation. Others resorted to attrition or experienced professional identity collapse. Our findings suggest that medical practitioners' experience of professional identity shifts can be improved by providing medical practitioners with opportunities for knowledge updates, better organizational leadership and work boundaries, strategies to enhance team cohesion, and other improvements to medical systems.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".