Stigma of visible and invisible chronic conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it