Overlap and differences between internalised stigma and depression: A Delphi study
Bibliographic record
Abstract
Two common consequences among persons with a physical health condition are internalised stigma associated with the physical illness and depression. Increased awareness and knowledge of both concepts might help professionals to better diagnose and deploy interventions that best suit the needs of people who experience these phenomena. Therefore, this study aimed to investigate to what extent and how internalised stigma and depression overlap. A Delphi study was conducted to create understanding and build consensus among experts. Over three survey rounds, experts in the field of stigma and depression (n = 24) were asked to determine whether signs/behaviours, symptoms/experiences, external factors and strategies or interventions were associated with internalised stigma, depression or both. Consensus was reached that internalised stigma and depression are common consequences of physical health conditions and are two separate but overlapping phenomena. The main difference between both were external factors, such as environmental (e.g. weather-driven mood fluctuations), or therapeutic (e.g. medication) aspects of depression. This study highlights the importance of raising awareness among professionals that internalised stigma and depression can co-occur, and that strategies and interventions are available for both. Improved awareness will lead to care that is more suitable to the individual’s needs, therewith improving quality of life.
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".