Toward Patient-Centered Care
Bibliographic record
Abstract
Clinicians rarely systematically document how their patients are feeling. Single item questions have been created to help obtain and monitor patient relevant outcomes, a requirement of patient-centered care.The objective of this review was to identify the psychometric properties for single items related to health aspects that only the patient can report (health perception, stress, pain, fatigue, depression, anxiety, and sleep quality). A secondary objective was to create a bank of valid single items in a format suitable for use in clinical practice.Data sources used were Ovid MEDLINE (1948 to May 2013), EMBASE (1960 to May 2013), and the Cumulative Index to Nursing and Allied Health Literature (1960 to May 2013).For the study appraisal, 24 articles were systematically reviewed. A critical appraisal tool was used to determine the quality of articles.Items were included if they were tested as single items, related to the construct, measured symptom severity, and referred to recent experiences.The psychometric properties of each item were extracted. Validity and reliability was observed for the items when compared with clinical interviews or well-validated measures. The items for general health perception and anxiety showed weak to moderate strength correlations (r = 0.28-0.70). The depression and stress items showed good area under the receiver operating characteristic curve of 0.85 and 0.73-0.88, respectively, with high sensitivity and specificity. The fatigue item demonstrated a strong effect size and correlations up to r = 0.80. The 2 pain items and the sleep item showed high reliability (intraclass correlation coefficient [ICC] = 0.85, κ = 0.76, ICC = 0.9, respectively).The search targeted articles about psychometric properties of single items. Articles that did not have this as the primary objective may have been missed. Furthermore, not all the articles included had the complete set of psychometric properties for each item.There is sufficient evidence to warrant the use of single items in clinical practice. They are simple, easily implemented, and efficient and thus provide an alternative to multi-item questionnaires. To facilitate their use, the top performing items were combined into the visual analog health states, which provides a quick profile of how the patient is feeling. This information would be useful for regular long-term monitoring.
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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 | 0.067 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".