Sickness certification as a complex professional and collaborative activity - a qualitative study
Bibliographic record
Abstract
BACKGROUND: Physicians have an important but problematic task to issue sickness certifications. A manifold of studies have identified a wide spectrum of medical and insurance-related problems in sickness certification. Despite educational efforts aiming to improve physicians' knowledge of social insurance medicine there are no signs of reduction of these problems. We hypothesised that the quality deficits is not only due to lack of knowledge among issuing physicians. The aim of the study was to explore physicians' challenges when handling sickness certification in relation to their professional roles as physicians and to their interaction with different stakeholders. METHODS: One hundred seventy-seven physicians in Stockholm County, Sweden, participated in a sick-listing audit program. Participants identified challenges in handling sick-leave issues and formulated action plans for improvement. Challenges and responsible stakeholders were identified in the action plans. To deepen the understanding facilitators of the program were interviewed. A qualitative content analysis was performed exploring challenge categories and categories of stakeholders with responsibility to initiate actions to improve the quality of the sick-listing process. The challenge categories were then related by their content to professional competence roles in accord with the Canadian Medical Education Directions for Specialists (CanMEDS) framework and to the stakeholder categories. RESULTS: Seven categories of challenges were identified. Practitioner patient interaction, Work capacity assessment, Interaction with the Social Insurance Administration, The patient's workplace and the labour market, Sick-listing practice, Collaboration and resource allocation within the Health Care System, Leadership and routines at the Health Care Unit. The challenges were related to all seven CanMEDS roles. Five categories of stakeholders were identified and several stakeholders were involved in each challenge category. CONCLUSIONS: Physicians performing sickness certification tasks experience a complex variety of challenges. From physician perspective actions to handle these need to be initiated in interaction with both medical and non-medical stakeholders. The relation between the challenges and a well-established professional competence framework revealed a complex pattern. Thus, from a public health perspective, educational activities aimed to improve the sick-listing process should address all physician competences including identification and interaction with stakeholders, and not just knowledge of social insurance medicine.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".