How Stigma and Discrimination Influences Nursing Care of Persons Diagnosed with Mental Illness: A Systematic Review
Bibliographic record
Abstract
One in four people in the world will be affected by mental illness in their lifetime, placing mental disorders as the leading cause of disability worldwide. This qualitative systematic review was to explore perceived stigma and discrimination experienced by individuals seeking care for physical or mental health concerns. Specifically, it sought to uncover the level of perceived stigma and discrimination experienced by mentally ill patients seeking care for physical or mental health concerns. Seven databases were searched between January 1, 2007 to November 1, 2018. Selected studies met the following inclusion criteria: 1) English language and published within North America, Australia, or United Kingdom; 2) studies and articles that consider individuals with mental illness seeking help for either mental or physical conditions in the hospital setting except for within mental health wards; and 3) research in which the phenomenon of interest examined how stigma and discrimination influences the perception of nursing care received by the mentally ill patient and the perception of nurses who provide care to the mentally ill patient. Eight studies met the inclusion criteria. Studies reported that both patients and nurses perceive similar barriers to person-centered care resulting from stigma toward mental illness. This significantly compromised quality person-centered care, and negatively affected the nurse-client relationship. Results indicate the need for further research to determine how health care and educational institutions play a role in perpetuating stigma against mental illness through the prioritization of physical illness over mental illness.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".