Understanding wellbeing from the perspectives of First Nations Australian youth: Findings from a national qualitative study
Bibliographic record
Abstract
Measures of wellbeing are increasingly used to inform critical decision making to support young people across a range of areas, including health, education, and programs and services that support them. Wellbeing is a culturally bound construct; therefore, robust wellbeing measures must ask about topics that are culturally relevant to and valued by the population of interest. Despite this, little attention has been directed at understanding the components of wellbeing relevant to, and valued by, First Nations youth. This project aims to redress this gap through a large national study conducted to gather the views of First Nations youth (aged 12-17 years) about what supports their wellbeing. First Nations youth were recruited in collaboration with partner organisations between May 2021 and September 2022 to participate in a PhotoYarning study (a combination of Photovoice and Yarning methodologies). Participants were given digital cameras, asked to take photos relevant to their wellbeing and then joined Yarning Circles to share and discuss the photos and their wellbeing. Yarning Circles were analysed using a strength based lens within a Collaborative Yarning approach. A total of 172 youth participated in one of 17 Yarning Circles sharing over 550 photographs. Analysis revealed an interconnected set of six themes foundational to their wellbeing, including: having a sense of belonging; feeling connected with others; receiving care and self-care; doing activities you enjoy; working towards goals and achievements; and having access to safe spaces . These findings provide a substantial foundation for building an understanding of wellbeing for First Nations youth, which can then form the basis of robust wellbeing measures, interventions, and programs to more effectively meet their wellbeing needs. • The study used a strengths-based Indigenist research approach, guided by the key principle of prioritising and privileging the voices and perspectives of First Nations youth. • PhotoYarning, an adaptation of Photovoice and Yarning methodologies, was used to explore what is important to, and impacts on, the wellbeing of First Nations youth. • First Nations youth were self-aware of fluctuations in their own wellbeing, the factors that support their wellbeing, and the value of striving for good wellbeing. • Six interconnected themes emerged across the parts of life that support the wellbeing of First Nations youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".