Patient and public involvement in the development and implementation of clinical practice guidelines: what do developers say?
Bibliographic record
Abstract
Rationale, aims and objectives: Two factors believed to improve clinical outcomes are patient involvement and the use of clinical practice guidelines (CPGs). This study aimed to improve strategies for increasing the involvement of patients by asking groups involved in CPG development to identify the various characteristics of programs they use for involving patients (PPIPs, or patient and public involvement programs) and what barriers and facilitators they perceive to this involvement.Method: Embedded within a systematic review aimed at identifying existing PPIPs in CPG development and implementation, we conducted semi-structured interviews with representatives of provincial, national and international organizations that have developed and/or implemented a CPG using a PPIP. Ten key informants (response rate of 71%) consented to participate. We performed thematic analyses on verbatim transcribed interviews.Results: Eleven key informants across 6 countries (response rate of 78%) consented to participate. The main barriers identified were recruitment difficulties, concerns with representativeness, participants’ lack of familiarity with scientific content and lack of financial resources. Key facilitators were training, provision of supporting documents, support staff, financial assistance for participants, their professional background and a high level of interest. Socio-political factors such as networks as well as government policy also had a major influence on patient and public participation.Conclusion: Our results identify and categorize issues that concern many CPG organizations about patient and public involvement and pave the way for the adoption of strategies that will improve patient and public participation in developing and implementing CPGs as healthcare moves towards a more person-centered model.
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 | 0.219 | 0.443 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".