Promoting the health of Aboriginal Australians through empowerment: eliciting the components of the Family well-being empowerment and leadership programme
Bibliographic record
Abstract
Most policies addressing Aboriginal health in Australia promote initiatives that are based on empowerment principles. Articulated programme components are necessary to support personal and group empowerment and to assist individuals in gaining the sense of control and purposefulness needed to exert their political and personal power in the face of the severe stress and powerlessness faced by the Australian Aboriginal people. This paper aims to provide a detailed description of the mechanisms underpinning a 'bottom-up' empowerment initiative, the Family well-being empowerment and leadership programme (FWB), and to analyze how the programme supports empowerment. The five stages of FWB were described and the validity of this model was assessed through the combination of participatory observation, documentation analysis, literature review, semi-structured interviews and iterative feedback with different analytical perspectives. Our study results articulated four distinct programme components: the setting plus inter-relational, educational and experiential actions. FWB is an example of the promotion of both outcome and process pathways towards empowerment. Potential applications of the programme are discussed.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".