Investigating a Question Prompt List to Support Patient-Centred Care for Women with Hypertensive Disorders of Pregnancy at Risk for Cardiovascular Disease
Bibliographic record
Abstract
Introduction: Hypertensive disorders of pregnancy (HDP) lead to increased cardiovascular disease (CVD) risk. Clinicians, and consequently women, are unaware of this link. Question prompt lists (QPL) may improve patient-centred care (PCC) by supporting discussions about HDP and CVD risk.Methods: This thesis included a scoping review to identify the characteristics of effective QPLs; and qualitative interviews to understand how a QPL might support PCC for women with HDP. Results: The scoping review included 53 studies. QPLs were most commonly 1 page long and had 38 questions. Interviews included 22 women with HDP. All women said a QPL would improve PCC, by raising awareness about HDP and CVD risk, helping them avoid clinician dismissal, and helping them prepare for consultations. Conclusions: This thesis contributes to a greater understanding of QPLs, and PCC for women with HDP, which may ultimately lead to improved PCC and equality in the healthcare system.
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 | 0.052 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".