Health preferences and decision‐making needs of disadvantaged women
Bibliographic record
Abstract
AIM: This paper reports the results of a survey of disadvantaged women in La Pintana, a municipality of Santiago, Chile, to determine their health decision-making needs. BACKGROUND: Research is needed as there is no published community-based study focusing specifically on health decision-making needs of disadvantaged women. METHODS: From April to November 1999, we conducted a cross-sectional interview survey of women registered at primary healthcare centres in La Pintana, an impoverished municipality of Santiago, Chile. RESULTS: The survey participants were 554 adult women over 15 years of age. Seventy-five percent reported making current health-related decisions. Types of decisions were primarily about navigation: where, when and from whom to seek care. The most common role in decision-making was sharing the decision with others, specifically husbands and other family members. Fifty-four percent experienced decisional conflict or uncertainty about options. Those reporting more manifestations of decisional conflict were more likely to lack information on available options, pros and cons of the options, and chances of benefits and harms associated with the options; they were also more likely to be unclear about what was important to them, to feel pressure from others, lack skill or ability in decision-making and be older. The most common strategies used when making all types of decisions were obtaining information on options and recommendations, and getting support from others. Participants preferred to receive information about options through counselling from their physicians, rather than nurses, from printed materials and from discussion groups of people facing the same decision. CONCLUSION: The majority of disadvantaged women were actively involved in decision-making and needed decision support to navigate the healthcare system. Nurses should play a more pivotal role in providing health decision support. This study needs to be replicated in other countries and cultural contexts.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".