Bibliographic record
Abstract
Attending to treatment preferences is an element of person-centred care, reported as beneficial in improving treatment adherence, satisfaction, and outcome, in practice. The results of preference trials were inconsistent in supporting these benefits in intervention evaluation research. Informed by the conceptualisation of treatment preferences positing their indirect impact on outcomes, this narrative review aimed to summarise the evidence on the effects of preferences on enrolment; withdrawal or attrition; engagement, enactment, and satisfaction with treatment; and outcomes. The search yielded 72 studies (57 primary trials and 15 reviews). The results of vote counting indicated that (1) offering participants the opportunity to choose treatment enhances enrolment (reported in 87.5% of studies), and (2) providing treatments that match participants' preferences reduces attrition (48%); enhances engagement (67%), enactment (50%) and satisfaction with (43%) treatment; and improves outcomes (35%). The results are attributed to conceptual and methodological issues including less-than-optimal assessment of treatment preferences, which contributes to ill-identified preferences, accounting for withdrawal, low enactment, and limited satisfaction with treatment. These treatment processes, in turn, mediate the impact of treatment preferences on outcomes. It is important to refine and standardise the methods for assessing preferences and to examine their indirect impact (mediated by treatment processes) on outcomes in future preference trials to validly identify their benefits.
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 | 0.013 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".