Exploring refugee children’s psychological needs through storytelling: A case study of two Latino American children.
Bibliographic record
Abstract
Research on mental health of refugee children frequently highlights the limitations of Western approaches to understand the reality of this population. The aim of this study was to explore the psychological needs of 2 refugee children, using a storytelling approach centered on needs. It combined the use of 2 projective tools, a “flower of needs” and a story, to explore the psychological needs of children in a safe and culturally sensitive way. This research sought to (a) identify the needs that children consider to be the most important through the construction of a flower of needs and (b) identify the needs that children address more frequently through the work with the story. For this purpose, a multiple case study design was conducted with 2 Latino/a children recruited from refugee families who had recently arrived in Canada. The children participated in 4 storytelling sessions. Data on the case presentation were collected from a semistructured interview with the parents. Data on the psychological needs exploration were collected from the children’s work with the flower of needs and the story across sessions. The results showed that both children identified physical comfort, connection, and peace as the most important psychological needs using the flower of needs. Connection, peace, and familiarity were the needs more frequently addressed working with the story. The results illustrate the potential of the flower of needs to assess needs and confirm the utility of storytelling to encourage a deep exploration of psychological needs with refugee children.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".