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Record W4417037979 · doi:10.1080/17441692.2025.2579686

Overlap and differences between internalised stigma and depression: A Delphi study

2025· article· en· W4417037979 on OpenAlexaff
Maartje Veerman, Anna T. van ‘t Noordende, Ruth M. H. Peters, Nicolas Rüsch, Wim H. van Brakel

Bibliographic record

VenueGlobal Public Health · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsPsychological interventionStigma (botany)Delphi methodMoodDepression (economics)Health professionalsHealth care

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.436
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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