Mental Health Work with Ukrainian Migrants: Reflections Using Maslow’s Theoretical Lens
Bibliographic record
Abstract
Within this paper I reflect upon the challenges I encountered as a result of the war in Ukraine, both personally as a Ukrainian-Canadian, and professionally, as a psychotherapist working with Ukrainian migrants. I begin by providing context on my personal experiences, and proceed to discuss my journey as a mental health practitioner engaging in support-group work in the Greater Toronto Area (GTA). I employ the theoretical framework of Abraham Maslow’s hierarchy of needs to provide a lens through which I can assess my own growth as an individual, in addition to providing a framework for my assessment of the progress among my support group participants. Key themes that this reflection will emphasize are the importance of holding a safe space for others, facilitating a “felt sense” of safety, and promoting a sense of belonging within a community. I conclude with a discussion of the benefits of applying Maslow’s theory in the context of psychotherapy, and suggest that the application of Maslow’s theory can be expanded upon within the helping professions more widely, specifically within the nursing field.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.031 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".