Bibliographic record
Abstract
Resource accumulation benefiting some people over others has been naturalized throughout human social evolution, even when its acquisition caused widespread suffering, disease, and premature death. The first religious orders, humanitarians, and indeed nurses, all emerged in response to this widespread exploitation and misery. In our globalized and technologically advanced world, there is widening inequality, deepening impoverishment, complex threats to health and security, and increasing uncertainty. As clinicians, we seek to assist people living in multi-dimensional states of suffering, yet our tools and methods are insufficient. Poverty measures often focus on monetary impoverishment alone; most are not meant to be broad or diagnostic in nature; political barriers and other perverse incentives limit the scope of poverty case finding; practice barriers (not least, time) limit case finding in the clinic; and often we focus on symptoms and surface manifestations of poverty – not the root causes. Grounded on an ontological foundation of critical realism and guided by an epistemology of critical theory, this study seeks to address this practice gap by synthesizing existing poverty measures and approaches via a literature scoping review; collecting primary data from health providers and clients through a generic qualitative research study; and constructing a novel diagnostic approach to multi-dimensional poverty with an emphasis on clinical utility. During eight months of data collection in urban Toronto, semi-structured interviews were carried out with 11 healthcare professionals and 6 clients, as well as a multidisciplinary focus group of 5 clinicians. Findings revealed that respondents held diverse perspectives on the definition of “poverty”, what to do about it, and what provider and client roles ought to be. Respondents also helped prioritize life domains, proposed ways to measure complex aspects of life, and suggested settings where a diagnostic approach to poverty would be useful. Based on these findings, I propose the Survival – Orientation – Self-Actualization (S.O.S.) Diagnostic Approach, a novel multi-dimensional measure infusing elements of the nursing process, person-centered care, and ethical triage to better guide poverty case finding and praxis in the clinical setting. The study concludes by offering suggestions on application to practice, informing policy, and suggestions for future research.
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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 | 0.040 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.008 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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".