Using Normalisation Process Theory to explore an interprofessional approach to Goals of Care: a qualitative study of stakeholders’ perspectives
Bibliographic record
Abstract
Goals of Care (GOC) is a longitudinal, multi-setting, and interprofessional practise aiming to guide the use of life-sustaining therapies. We documented the perspectives of different stakeholders regarding their roles in GOC intervention and explored the possibility of implementing an interprofessional approach in a healthcare and social services institution. We interviewed nurses, social workers, and relatives of deceased persons and analyzed the results using an analytical framework based on the 16 mechanisms of the Normalization Process Theory. We identified barriers to implementing a sustainable interprofessional approach to GOC, such as the lack of designated leaders responsible for supporting the day-to-day provision of this rather complex intervention, the difficulty of access to physicians in two of the three care settings under study, and the lack of a robust informational system. We also demonstrated that the GOC intervention is postponed until there is no uncertainty to deal with, i.e., at the end of life. Our study adds to an emerging body of literature criticising the concept of making advance medical directives itself. We advocated for the promotion of tools that would enable lay people to select and empower a supportive decision maker to better represent them in serious illness decision making.
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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 | 0.055 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".