Homelessness: Perspectives, Misconceptions, and Considerations for Occupational Therapy
Bibliographic record
Abstract
SUMMARY Like poverty, the problem of homelessness has been with us to varying degrees since the founding of our nation. Attempts to explain homelessness have an equally long history. Hence, the literature and popular media are home to divergent perspectives, explanations, and characterizations of homelessness. The objectives of this paper are to present a unifying taxonomy of prominent perspectives on homelessness, and to illustrate how various perspectives lead to particular characterizations of persons who become homeless. The taxonomy traces the connection between perspectives and interpretations of the problem and helps to illuminate implicit and often unexamined assumptions about who becomes homeless and why. Critical examination of these perspectives is vital because our individual and collective understanding of homelessness is a powerful determinant of how we approach occupational therapy practice with this population. Implications for community practice and program planning for individuals and families in homeless shelters are also discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".