Applying health determinants and dimensions in social work practice
Bibliographic record
Abstract
Dimensions and determinants related to physical health, mental health and well-being can be used as tools in social work practice. These frameworks are useful for assessment, planning and intervention activities in complex client situations, alerting social workers to consider diverse features and processes that influence well-being in people's lives. They can be applied in social work with individuals, families, groups, and communities. In our view health and well-being need to be viewed holistically, broadly and in each unique situation, changing over time over the life course. Literature based on key word searches of concepts we use in our teaching and research and that has informed our conceptualisation of dimensions and determinants in physical health, mental health and well-being, is reviewed. A case illustration is presented to illustrate how the dimensions and determinants are used to inform practice and compare and apply alternative frameworks. Finally, the implications of these and other frameworks for social work, not only in health care settings, but also in other fields of social work practice, are discussed.
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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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".