Effectiveness and perceptions of using templates in long-term condition reviews: a systematic synthesis of quantitative and qualitative studies
Bibliographic record
Abstract
BACKGROUND: Review templates are commonly used in long-term condition (LTC) consultations to standardise care for patients and promote consistent data recording. However, templates may affect interactions during the review and, potentially, inhibit patient-centred care. AIM: To systematically review the literature about the impact that LTC review templates have on process and health outcomes, and the views of health professionals and patients on using review templates in consultations. DESIGN AND SETTING: Parallel qualitative and quantitative systematic reviews. METHOD: Following Cochrane methodology, nine databases were searched (1995-2019; updated July 2020) for clinical trials and qualitative studies of LTC templates in healthcare settings. Duplicate selection, risk-of-bias assessment, and data extraction were performed. The quantitative and qualitative analyses were conducted in parallel, and findings synthesised narratively. RESULTS: In total, 12 qualitative and 14 quantitative studies were included (two studies reported both qualitative and quantitative data, and were included in both analyses). Review templates were well used, but the only study to assess health outcomes showed no effect. Templates can improve documentation of key measures and act as a reminder tool; however, they can restrict the review process, and risk health professionals' agendas being prioritised over those of patients. Templates may also limit opportunities to discuss individuals' concerns about living with their condition and act as a barrier to providing patient-centred care. CONCLUSION: Future research should evaluate health, as well as process, outcomes. The potential benefits of templates in improving documentation should be balanced against concerns that 'tick boxes' may override patient agendas, unless templates are designed to promote patient-centred care.
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 | 0.391 | 0.618 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".