Learning to Work with Immigrant Families: An Experiment in Experiential Learning
Bibliographic record
Abstract
This study examined what students in three professional programs – Nursing, Social Work, and Early Childhood Studies – could learn about working with immigrant families using narrative inquiry as a heuristic device. Data collected from the students in focus groups demonstrated their capacity for ethical caring by recognizing individual characteristics of immigrant families, becoming more self-aware in interactions with them, and noticing institutional practices from the families’ perspectives. The students also began to realize the uncertainties of professional practice, which could help promote the habit of reflection. Findings suggest that the experiment was worthwhile, albeit limited by self-reported data, a small sample, and a short duration. Dans cette étude, nous examinons ce que les étudiants inscrits dans trois programmes professionnels – soins infirmiers, travail social et études de la petite enfance – pourraient apprendre sur le travail avec des familles d’immigrants par le biais de l’enquête narrative en tant qu’instrument heuristique. Les données recueillies auprès des étudiants réunis en groupes de discussion ont indiqué que ceux-ci avaient prouvé leur aptitude à l’empathie éthique en reconnaissant les caractéristiques individuelles des familles d’immigrants, en devenant davantage conscients de leurs interactions avec ces familles et en prenant conscience des pratiques institutionnelles à partir du point de vue de ces familles. Les étudiants ont également commencé à comprendre les incertitudes de la pratique professionnelle, ce qui pourrait favoriser de meilleures habitudes de réflexion. Les résultats suggèrent que l’expérience était appréciable, bien qu’elle ait été limitée par des données auto-déclarées, un échantillon limité et une courte durée.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".