Bibliographic record
Abstract
Nurses deliver care to people with various forms of chronic illnesses and conditions. Some chronic conditions, such as paraplegia, are visible while others, such as diabetes, are invisible. Still others, such as multiple sclerosis, are both visible and invisible. Having a chronic illness or condition and being different from the general population subjects a person to possible stigmatization by those who do not have the illness. Coping with stigma involves a variety of strategies including the decision about whether to disclose the condition and suffer further stigma, or attempt to conceal the condition or aspects of the condition and pass for normal. We present a beginning framework that describes the relationship between the elements of stigma and the decision to disclose or hide a chronic condition based on its visibility or invisibility. The specific aims were to combine the results from a meta-study on qualitative research with a review of the quantitative literature, then develop a theoretical framework. Although an understanding of how patients cope with stigmatizing conditions is essential for nurses who aim to deliver comprehensive individualized patient care, there is little current literature on this subject. The relationship between visibility and invisibility and disclosure and non-disclosure remains poorly understood. A framework to facilitate a deeper understanding of the dynamics of chronic illnesses and conditions may prove useful for practice.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".