Single item measures of self-rated mental health: a scoping review
Bibliographic record
Abstract
BACKGROUND: A single-item measure of self-rated mental health (SRMH) is being used increasingly in health research and population health surveys. The item asks respondents to rate their mental health on a five-point scale from excellent to poor. This scoping study presents the first known review of the SRMH literature. METHODS: Electronic databases of Medline, CINAHL, PsycINFO, EMBASE and Cochrane Reviews were searched using keywords. The databases were also searched using the titles of surveys known to include the SRMH single item. The search was supplemented by manually searching the bibliographic sections of the included studies. Two independent reviewers coded articles for inclusion or exclusion based on whether articles included SRMH. Each study was coded by theme and data were extracted about study design, sample, variables, and results. RESULTS: Fifty-seven studies included SRMH. SRMH correlated moderately with the following mental health scales: Kessler Psychological Distress Scale, Patient Health Questionnaire, mental health subscales of the Short-Form Health Status Survey, Behaviour and Symptom Identification Scale, and World Mental Health Clinical Diagnostic Interview Schedule. However, responses to this item may differ across racial and ethnic groups. Poor SRMH was associated with poor self-rated health, physical health problems, increased health service utilization and less likelihood of being satisfied with mental health services. Poor or fair SRMH was also associated with social determinants of health, such as low socioeconomic position, weak social connections and neighbourhood stressors. Synthesis of this literature provides important information about the relationships SRMH has with other variables. CONCLUSIONS: SRMH is associated with multi-item measures of mental health, self-rated health, health problems, service utilization, and service satisfaction. Given these relationships and its use in epidemiologic surveys, SRMH should continue to be assessed as a population health measure. More studies need to examine relationships between SRMH and clinical mental illnesses. Longitudinal analyses should look at whether SRMH is predictive of future mental health problems.
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 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 | 0.028 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".