144 Qualitative component of a longitudinal, mixed methods programme evaluation using in-depth interviews
Bibliographic record
Abstract
<h3>Background</h3> Macmillan Specialist Care at Home seeks to enhance patient-centred care through community and home-based palliative and end of life care services. This article reports a qualitative study that formed part of a larger evaluation of a multi-site implementation of the specialist palliative care community service. <h3>Aim</h3> To examine caregiver, patient and carer experiences of service implementation and identify how its key features: early referral; clinical interventions at home; avoiding hospital admissions; and facilitating patients to die in their preferred place of death, worked in practice. <h3>Methods</h3> Qualitative component of a longitudinal, mixed methods programme evaluation using in-depth interviews (n=49 [Health professionals (n=18), volunteers (n=14), patients (n=9) and lay carers (n=8) from six community-based sites]) supported by a visual research method 9Pictor9 to facilitate dialogue about experiences of care. The data were analysed using thematic analysis. <h3>Results</h3> Effective partnerships between generalist and specialist teams improve the overall quality of community and home-based palliative and end of life care. The collaborative approach instils confidence and empowers patients and carers, principal factors in crises-prevention and enabling home deaths. Key themes were: Early referral and rapport; Benefits of seamless care. Averting crises situations; Community consultant as catalyst; Home-based clinical interventions Attending to wider aspects of care; Managing expectations through patient and carer education. <h3>Conclusions</h3> Macmillan Specialist Care at Home is a complimentary resource for community-based palliative and end of life care that can extend and greatly enhance the quality of care experience for people with life limiting illnesses and their families. Joint education, training and continuing professional development for specialist teams and existing community staff is advocated to promote shared knowledge and to forge and strengthen bonds between specialist and generalist health and social care professionals. Further testing of the mechanisms involved in implementation will improve transferability potential.
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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 | 0.043 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".