<b>‘</b>Our culture makes us strong’: Understanding and working with community strengths among Aboriginal people in western Sydney
Bibliographic record
Abstract
BACKGROUND: Strengths-based approaches to health care are often seen as an alternative to deficit-based approaches and are common in Aboriginal health settings. Despite this, there is little existing research that describes Aboriginal peoples' perspectives about the strengths of their communities. This paper describes cultural strengths and resources as understood by Aboriginal people living in western Sydney. METHODS: In-depth interviews were used to collect qualitative data from two communities on Dharug and Dharrawal Country in western Sydney Australia. Data come from a larger study, which focused on how cultural strengths supported sexual well-being. Fifty-two interviews were conducted with Aboriginal young people (aged 16-24 years) by trained peer interviewers. Additionally, 16 interviews with Aboriginal adults (25 years and older) were conducted by members of the research team. FINDINGS AND DISCUSSION: While opinions varied, four key areas of cultural strength were identified: (1) strong kinship relationships; (2) knowledge sharing; (3) shared experiences, identities, and values; and (4) knowing Country. Throughout these four themes, the sense of connection and belonging is viewed as an important overarching theme. CONCLUSION: Communities are not homogenous with regard to what they view as cultural strengths. Knowing Country and practising culture meant different things to different individuals while providing a similar sense of belonging, connection, and identity. SO WHAT: Health service providers, policies, and programs can use this information to understand the continuing impacts of past policies and events whilst recognising that each community has strengths that can be drawn upon to improve service engagement, knowledge sharing, and health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".