Decisional needs assessment of patients with complex care needs in primary care: a participatory systematic mixed studies review protocol
Bibliographic record
Abstract
INTRODUCTION: Patients with complex care needs (PCCNs) often suffer from combinations of multiple chronic conditions, mental health problems, drug interactions and social vulnerability, which can lead to healthcare services overuse, underuse or misuse. Typically, PCCNs face interactional issues and unmet decisional needs regarding possible options in a cascade of interrelated decisions involving different stakeholders (themselves, their families, their caregivers, their healthcare practitioners). Gaps in knowledge, values clarification and social support in situations where options need to be deliberated hamper effective decision support interventions. This review aims to (1) assess decisional needs of PCCNs from the perspective of stakeholders, (2) build a taxonomy of these decisional needs and (3) prioritise decisional needs with knowledge users (clinicians, patients and managers). METHODS AND ANALYSIS: This review will be based on the interprofessional shared decision making (IP-SDM) model and the Ottawa Decision Support Framework. Applying a participatory research approach, we will identify potentially relevant studies through a comprehensive literature search; select relevant ones using eligibility criteria inspired from our previous scoping review on PCCNs; appraise quality using the Mixed Methods Appraisal Tool; conduct a three-step synthesis (sequential exploratory mixed methods design) to build taxonomy of key decisional needs; and integrate these results with those of a parallel PCCNs' qualitative decisional need assessment (semistructured interviews and focus group with stakeholders). ETHICS AND DISSEMINATION: This systematic review, together with the qualitative study (approved by the Centre Intégré Universitaire de Santé et Service Sociaux du Saguenay-Lac-Saint-Jean ethical committee), will produce a working taxonomy of key decisional needs (ontological contribution), to inform the subsequent user-centred design of a support tool for addressing PCCNs' decisional needs (practical contribution). We will adapt the IP-SDM model, normally dealing with a single decision, for PCCNs who experience cascade of decisions involving different stakeholders (theoretical contribution). Knowledge users will facilitate dissemination of the results in the Canadian primary care network. PROSPERO REGISTRATION NUMBER: CRD42015020558.
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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 | 0.169 | 0.141 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.013 |
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".