Enhancing Low‐Income Parents' Capacities to Promote Their Children's Health: Education Is Not Enough
Bibliographic record
Abstract
In 1996 the Capital Heath Region in Edmonton, Alberta integrated a primary health care component into Head Start programs. One aspect of the primary health care component (PHC-HS) was a series of education sessions aimed at strengthening parents' capacities to enhance their children's health. To make the education sessions relevant, 10 focus groups with 65 parents of children who attended Head Start were conducted prior to the sessions. Findings indicated that participants' ability to enhance their children's health and manage their children's illnesses was limited as much by low incomes, inadequate health care coverage, and lack of transportation as it was by a lack of knowledge. Results provide evidence that health education sessions alone are not adequate to significantly enhance low-income parents' capacities to promote their children's health. Efforts to strengthen the abilities of low-income individuals and families to promote their health will be most effective if health education is accompanied by policy advocacy and social action strategies that challenge the socioeconomic and political conditions that negatively affect health. Public health nursing's commitment to social justice, as well as findings about the limitations that low incomes, inadequate health care benefits, and lack of transportation placed on participants' ability to enhance their children's health, underscore the need for public health nurses (PHNs) to address structural conditions contributing to health inequities. As such, an overview of literature that details strategies and theoretical models for challenging socioeconomic and political conditions which restrict the ability of low-income individuals and families to enhance their health is provided.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".