Experience of being a frequent user of primary care and emergency department services: a qualitative systematic review and thematic synthesis
Bibliographic record
Abstract
BACKGROUND: Frequent users of healthcare services are often categorised as 'heavy-cost patients'. In the recent years, many jurisdictions have attempted to implement different public policies to optimise the use of health services by frequent users. However, throughout this process, little attention has been paid to their experience as patients. OBJECTIVE: To thematically synthesise qualitative studies that explore the experience of frequent users of primary care and emergency department services. DESIGN: Qualitative systematic review and thematic synthesis. SETTING: Primary care and emergency department. PARTICIPANTS: Frequent users of primary care and emergency department services. METHODS: A qualitative systematic review was conducted using three online databases (MEDLINE with full text, CINAHL with full text and PsycINFO). This search was combined to an extensive manual search of reference lists and related citations. A thematic synthesis was performed to develop descriptive themes and analytical constructs. STUDY SELECTION: Twelve studies were included. All included studies met the following inclusion criteria: qualitative design; published in English; discussed frequent users' experiences from their own perspectives and users' experiences occurred in primary care and/or emergency departments. RESULTS: The predominant aspects of frequent users' experiences were: (1) the experience of being ill and (2) the healthcare experience. The experience of being ill encompassed four central themes: physical limitations, mental suffering, impact on relationships and the role of self-management. The healthcare experience embraced the experience of accessing healthcare and the global experience of receiving care. CONCLUSION: This synthesis sheds light on potential changes to healthcare delivery in order to improve frequent users' experiences: individualised care plans or case management interventions to support self-management of symptoms and reduce psychological distress; and giving greater importance on the patient-providers relationship as a central facet of healthcare delivery. This synthesis also highlights future research directions that would benefit frequent users.
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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 | 0.074 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".