Social stigma associated with cancer in the Newfoundland and Labrador population: an exploratory study
Bibliographic record
Abstract
Abstract Background: Among all Canadians, residents of the province of Newfoundland and Labrador (NL) have the highest risk of developing and dying of cancer. Effects of cancer-associated stigma and discrimination can contribute to the negative consequences of cancer and unnecessarily burden individuals diagnosed with cancer. In this study, we aimed to examine stigma and discrimination-related experiences of individuals diagnosed with cancer and predictors of experiencing stigma in NL. Methods: This was a cross-sectional and self-administered online survey study. The survey instrument included both open-ended and closed-ended items, and data were collected between June 2019 and February 2020. Descriptive statistics, thematic analyses, and regression techniques were used for data analysis. Results: A total of 325 respondents participated in this study. Self-perceived stigmatization and discrimination were reported by 24% and 14% of the participants, respectively. The most common sources contributing to these experiences were friends, insurance and financial companies, and workplace relations. Issues related to insurance, social relations, and workplace opportunities were among the most common reported impacts of cancer. A large portion of the participants had not experienced stigma and discrimination or experienced anything but positive support from others. Several factors associated with experiencing stigma were also identified, such as age, disease stage, ethnicity, and socioeconomic status. Misconceptions related to cancer, issues with insurance companies, and change of life after cancer were the top themes identified by thematic analysis. Conclusions: This study identified a rich overview of cancer-associated lived experiences in the NL population. Educational campaigns on cancer, integration of stigma-related support in cancer care, and stronger antidiscriminatory legislations and practices should be encouraged in NL.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".