Exploring the professional and personal experiences of mental health service providers in rural British Columbia
Bibliographic record
Abstract
Accessing mental health-related support in rural communities can be challenging due to the limited availability and accessibility of services. Moreover, the increase in frequency and severity of climate change events (CCEs; e.g., wildfires, floods), which disproportionately affect rural communities, have increased demand for rural-based mental health services. While there is a paucity of research regarding the experiences of mental health service providers (MHSPs) in these communities, there is evidence that they often face rural-specific challenges in their roles and experience high rates of burnout. They may be also be affected directly by CCEs, limiting their capacity to provide support. By increasing our understanding of the professional and personal experiences of rural MHSPs, the mental health needs of rural communities can be better addressed. Using interpretive descriptive methodology, this thesis explored the experiences of MHSPs throughout rural BC. Participants (N = 125) completed an online survey that explored their personal and professional experiences in general and in relation to CCEs. Inferential statistics and thematic analysis were used to analyze quantitative and open-ended data, respectively. Both accredited (e.g., psychologists, counsellors) and non-accredited (e.g., community support workers) MHSPs participated. Participants offered numerous services, with counselling/psychotherapy being the most common. Higher levels of distress was associated with greater burnout. Non-accredited MHSPs reported higher levels of anxiety and burnout. Most participants had experienced multiple CCEs. Those whose work had been impacted by CCEs, experienced more anxiety, stress, and burnout. Analysis of the open-ended questions revealed three inter-related themes: ‘living in rural BC,’ ‘being a MHSP in Rural BC,’ and ‘the impact of CCEs’. The first theme reflects features inherent to living and working in a rural community. Factors related specifically to providing mental health services in these communities were addressed by the second theme. The third theme highlighted the personal and professional impacts of CCEs on rural MHSPs. The identified needs of rural MHSPs and strategies for addressing these needs are discussed. It is hoped that this will enhance their wellbeing, as well as mental health support for rural community members.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".