The Influence of Context and Practitioner Attitudes on Implementation of Person‐Centered Assessment and Support for Family Carers Within Palliative Care
Bibliographic record
Abstract
BACKGROUND: The Carer Support Needs Assessment Tool (CSNAT) intervention is an evidence-based, person-centered approach to carer assessment and support within palliative care. As such, it requires a change in practice from a practitioner- to a carer-led assessment and support process. A paucity of research has investigated factors affecting implementation of evidence-based interventions within palliative care. OBJECTIVE: To examine differences between high and low adopters of the CSNAT intervention in terms of practitioner attitudes to the intervention and organizational context. METHODS: Phase IV study of the implementation of the CSNAT intervention at scale in 36 UK palliative care services over 6 months. Survey at baseline and 6 months of practitioners at implementation sites, informed by the Promoting Action on Research Implementation in Health Services (PARIHS) Framework. Survey tools: (a) questionnaire to assess attitudes to the CSNAT intervention; (b) Alberta Context Tool (ACT) to assess organizational context. Monthly data on intervention use enabled service classification as "high" or "low" adopters. RESULTS: Surveys returned at baseline were 157/462 and at 6 months were 69/462. Compared with low adoption services, high adopters were more likely to be hospice, at home, and day services; have a higher ratio of internal facilitators to total staff numbers; and higher scores for ACT "informal interactions" denoting more discussions about care between colleagues. Both had similarly positive attitudes to the CSNAT intervention pre-implementation, but by 6 months low adoption services developed significantly more negative attitudes, while high adoption services attitudes mostly remained the same or improved. LINKING EVIDENCE TO ACTION: Implementation may be more successful for services that offer regular opportunities to use the intervention in practice, have sufficient levels of facilitators, stimulate more staff discussion, and encourage maintenance of positive motivation. Implementation of person-centered interventions needs to plan for such factors. This has informed an implementation toolkit for the CSNAT intervention.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".