Exploring readiness for implementing goal-oriented care in primary care using normalization process theory
Bibliographic record
Abstract
AIM: To use normalization process theory (NPT) to build a strategy for the implementation of goal-oriented care (GOC) in primary care in Flanders, Belgium. BACKGROUND: GOC is a possible approach to more coordinated and integrated care and tailors care to patients' personal life goals. The concept has gained interest among policy makers and researchers, but the main drivers for successful implementation are the primary healthcare professionals (PHCPs) who need to see added value of GOC in order to embed it into their daily practice. NPT, developed to understand the processes of implementing new ways of organizing care, offers a useful lens to understand adoption of GOC in primary care practice. METHOD: = 131) who participated in a 2-hour community meeting on GOC were asked to complete the Normalization MeAsure Development survey. This 23-item survey is based on NPT and describes participants' views about how an intervention would impact their work, their expectations about it, and whether it could become a routine part of their work. FINDINGS: The NPT constructs coherence (sense-making work) and cognitive participation (relational work) showed positive tendency toward implementation of GOC. The participants had an initial understanding on GOC and there was much interest in supporting and start working with this approach. The other constructs collective action (operational work) and reflexive monitoring (appraisal work) will need further efforts to trigger implementation. A common ground is needed to integrate GOC as a common practice which can be achieved by intensive interprofessional collaboration.
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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".