Envisioning Pathways to Community Health Through the Eyes of North End Haligonians
Bibliographic record
Abstract
There are many populations poorly served by the current Canadian approach to health and illness care. These populations include members of ethnic minority groups, those with poor socio-economic status, those who are homeless, working in the sex trade or affected by mental illness. One way of potentially improving the health of communities or populations is through policy. In addition, a deeper understanding of the health needs of underserved populations could facilitate expeditious solutions mindful of resource challenges. In spite of copious research, health inequities and disparities persist. My hypothesis was that the conscious use of specific lenses to examine policies or interview data was a useful device to both better visualize and understand actions related to policy development and community member input. Thus, this thesis research was comprised of two major components. The first was an exploration of two policies of an academic tertiary health care centre through the lenses of feminist, critical social and systems theory. The objective was to determine if viewing policy development using different lenses might influence thinking about issues related to underserved populations. The second component used the same three lenses to conduct a grounded theory analysis of eleven photo-elicitation and six photo-voice interviews with North End Halifax Community members. The focus of these interviews was on contributions to health found in the community of North End Halifax. The results clearly show that examination of policies through the three theoretical lenses serves to highlight hidden assumptions and to broaden the view and comprehension of implications and potential impacts of policies. A better way to formulate policies is one step towards achievement of improved health outcomes. The use of three lenses in the grounded theory analysis significantly enhanced the depth of interview analysis: the feminist lens accentuated the concept of caring and relationships and the extent of the White-middle-class male perspective; the critical social lens crystallized the power disparities at play; and the systems lens stressed the need to examine root causes. The conscious use of specific lenses could facilitate a more comprehensive and comprehended view of the health needs of underserved populations.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".