Understanding the Experiences of Fly-In/Fly-Out Mental Health Service Providers in the Inuit Nunangat Region
Bibliographic record
Abstract
My Master's thesis examined the experiences of fly-in and fly-out (FIFO) mental health service providers in Inuit Nunangat.Through participatory action research and semi-structured interviews with eight FIFO mental health service providers who deliver services to various Inuit communities across Inuit Nunangat, I assessed barriers and enablers to FIFO counselling, and I co-developed recommendations to ensure optimal delivery of services with my partner organization.I examined the factors that influence experiences of vicarious trauma for providers and gained insight into ways that FIFO practices may mitigate the effects of vicarious trauma.Additionally, I explored the impacts of the COVID-19 pandemic on FIFO mental health service delivery.The findings enabled us to reconceptualize mental health service delivery with considerations to mitigate pandemic risks.Together, these two papers are a novel contribution to understanding the experiences of FIFO mental health service providers in northern Canada.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".