Mind matters: A model for mental health awareness and support from the Geological Society of London
Bibliographic record
Abstract
Up to one in four UK adults now experience a mental health issue each year. Meanwhile, the numbers of UK university students reporting a mental health condition rose by a factor of five between 2006 and 2016, reaching two percent, with some higher education institutions reporting that one in four students have accessed or are waiting to access mental health services. There are a number of aspects of work and study in the geological sciences that can contribute to or exacerbate poor mental health, with fieldwork identified as a particular source of stress and worry for students and professionals alike. Without clearly signposted pathways to support mental health in the geosciences, students and professionals may choose to leave the field. In 2019, the Geological Society of London launched a mental health and wellbeing programme for its own staff, and is now sharing the model, and lessons learned during implementation, with geologists and employers of geologists. Following a mental health awareness course made available to all staff, staff were encouraged to apply to become a certified mental health first aider and/or to serve on the newly created Mental Health and Wellbeing Group. Over a quarter of staff members applied for one or both positions, with 20 percent selected for the group, and four of those members selected to become mental health first aiders. In addition, a member of the senior leadership team trained as a mental health champion. We have also launched a survey of employee attitudes toward and understanding of mental health, and started to deliver a series of stress-reducing activities. Early results include staff members reporting feeling more valued as people and an increased uptake of services offered through the employee assistance programme, which offers confidential support around mental and physical health. We will also assess changes in employee morale and sickness absence following the introduction of the programme. Finally, we offer strategies for proposing and implementing mental health and wellbeing programmes at other geoscience employers.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".