Is poor mental health an unrecognised occupational health and safety hazard for conservation biologists and ecologists? Reported incidences, likely causes and possible solutions
Bibliographic record
Abstract
Workers in many professions suffer from poor mental health as a result of their employment. Although a bibliographic search generated little published evidence for poor mental health among conservation biologists and ecologists, the phenomenon has been reported among researchers working on coral reefs, climate change, wildfires and threatened species. Factors responsible for poor mental health include (1) epistemic attributes associated with conservation biologists’ and ecologists’ deep knowledge base; (2) non-epistemic values associated with their view of the natural world; and (3) a complex suite of factors relating to the wider social, political and economic milieu in which they practise their trade. Because it relates directly to employment, poor mental health among conservation biologists and ecologists must be differentiated from the phenomena of ‘environmental grief’ and ‘solastalgia’ reported in the wider community. A number of solutions to the problem have been suggested, including appreciating the conservation successes that have been achieved, recognising the importance of collegiality and comradeship, acknowledging the role of grieving rituals, active intervention via therapeutic counselling, reducing the incidence of censorship and repression of scientists’ research, and the adoption of a Stoic view of the world. I propose a different approach: conservation biologists and ecologists should reposition their personal experiences within an historical perspective that sees them as part of a long tradition of struggle to protect the natural environment. An apt rallying cry to help conservation biologists and ecologists manage their mental health is Pablo Casals’ ‘The situation is hopeless. We must take the next step’.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".