Exploring the Decision-Making Experiences of Culturally and Linguistically Diverse Caregivers and Autism Service Providers: An Interpretative Description
Bibliographic record
Abstract
This dissertation explores the decision-making experiences of culturally and linguistically diverse caregivers and autism service providers when agreeing upon autism interventions. It aims to understand the cultural and contextual preferences, values, and beliefs that influence these decisions in the context of autism services. Using an interpretative description approach, my research involved in-depth semi-structured interviews with seven caregivers and seven service providers in Alberta, Canada. The data analysis followed the interpretative description methodology to explore the lived experiences of both groups. The findings revealed that providers are hesitant in their interactions with culturally and linguistically diverse caregivers to ask questions supporting culturally informed practices and identify needs for additional support. The cultural preferences, values, beliefs, and contextual influences were seldom discussed when culturally and linguistically diverse caregivers in this study reflected on their experiences with autism service providers. When culturally and linguistically diverse caregivers received culturally tailored services, they felt valued and understood, enhancing their overall satisfaction and trust in the services provided. In contrast, autism service providers who offered tailored services felt it improved their understanding of meeting the specific needs of the family and the child. The decision-making experiences of culturally and linguistically diverse caregivers significantly point to the influence of acculturation experiences. Recognizing and addressing these experiences is crucial for effective decision-making. Providers require resources to incorporate cultural and contextual factors into their intervention and service decisions to enhance outcomes for families from different cultures. Following the qualitative study, a toolkit was developed for providers in autism services. This toolkit emphasizes the decision-making process and includes specific questions that providers can ask to guide their interactions with caregivers. It also offers strategies for initiating conversations about culture and context relevant to culturally and linguistically diverse caregivers. Additionally, the toolkit provides links to various resources to support further learning for autism service providers. To facilitate the dissemination of this toolkit, a presentation was created to assist the leadership of an autism service organization in sharing it with their providers.
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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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".