Engaging under- and/or never-engaged populations in health services: A systematic review
Bibliographic record
Abstract
Patient engagement is a mechanism used to facilitate person-centred care, however, has not been realized in all patient populations. Often, many marginalized populations still remain under- and/or never-engaged. The purpose of this systematic review was to: 1) identify methods or interventions that have been used to engage under- and/or never-engaged populations in health services and 2) identify outcomes that are associated with engaging under- and/or never-engaged populations in health services. A comprehensive search using the Ovid MEDLINE, EMBASE and CINAHL databases was conducted to examine literature between January 2002 and January 2015. Twenty-nine studies met the inclusion criteria. Data was extracted from these studies and findings are synthesized based on discrete themes that map to the research objectives. The majority of studies were quantitative, repeated-measures designs and concentrated in the United States. Hispanic and Latino/a populations were most frequently included in these studies. The main methods of recruitment included: 1) referral from a healthcare provider, 2) patient self-referral after seeing advertisements on mass media or targeted media, 3) directly approached by researcher in-person or telephone, and 4) administrative databases. Interventions occurred primarily at the individual-level, however some system-level interventions were identified. Five main outcomes resulted from the interventions, including: 1) behavioural change, 2) physiological, 3) psychosocial, 4) system and 5) process. Finally, culture-specific components were embedded in the interventions, both as surface and deep structures. This study provides future direction for patient engagement related projects, as it relates to under-and never-engaged population in healthcare. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".