Work-related impacts on doctors’ mental health: a qualitative study exploring organisational and systems-level risk factors
Bibliographic record
Abstract
Background Protecting doctors’ mental health has typically focused on individuals, rather than addressing organisational and structural-level factors in the work environment. Objectives This study uses the socioecological model (SEM) to illuminate and explore how these broader factors inform the mental health of individual doctors. Design Semi-structured interviews (20–25 hours) and ethnographic observations (90 hours) involving work shadowing doctors (n=14). Participants Doctors representing various career stages, specialty areas, genders and cultural backgrounds. Setting Three specialties in a public South Australian hospital. Thematic analysis revealed work-related risk factors for poor mental health. Results The SEM framework was used to analyse the work environment’s impact on doctors’ mental health. The analysis identified how the layers interconnect to influence risk factors for individual doctors. Microsystem : lack of control over career advancement, disenfranchisement due to understaffing and concerns about handling complex cases relative to experience. Mesosystem : negative impacts of shift work and fragmented teams, leading doctors to absorb pressure despite exhaustion to maintain professional credibility. Exosystem : high patient loads with time constraints and geographical limitations hindering care delivery, compounded by administrative burdens. Macrosystem : the commercialisation of medicine emphasising corporatisation and bureaucratic processes, which devalues professional autonomy. Conclusions This study highlights how doctors experience layers of interconnected factors that compromise their mental health but over which they have very little control. Interventions must therefore address these issues at organisational and systemic levels, for which starting points evident within our data are identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".