Primary care transformation in Scotland: a qualitative study of GPs’ and multidisciplinary team members’ views
Bibliographic record
Abstract
BACKGROUND: The Scottish Government's vision to transform primary care includes expansion of the primary care multidisciplinary team (MDT), formalised in the new GP contract in April 2018. AIM: To explore practitioners' views on the expansion of MDT working in Scotland. DESIGN AND SETTING: Qualitative study with GPs and a range of MDT staff working in three different population settings in Scotland. METHOD: In-depth semi-structured interviews were carried out by telephone with 8 GPs and 19 MDT staff between May and June 2022. Interviews were audio-recorded and transcribed verbatim. Thematic analysis was conducted to identify commonalities and divergences in the interviews. RESULTS: Internal challenges facing MDT staff included adapting to the fast pace of primary care, building new relationships, training and professional development needs, line management issues, and monitoring and evaluation of performance. External challenges included the ongoing effects of the COVID-19 pandemic, lack of time, difficulties with hybrid working, and low staff morale. Most GPs reported that expansion of their roles as expert medical specialists had not yet happened because their workload had not decreased (and in many cases had increased). In deprived areas, insufficient resources to deal with the high numbers of patients with complex multimorbidity remained a key issue. Interviewees in remote and rural settings felt the new contract did not take into account the unique challenges of providing primary care services in such areas, and recruitment and accommodation were cited as particular problems. CONCLUSION: Although there has been substantial expansion of the primary care MDT, which most GPs welcome, many challenges to effective implementation remain that must be addressed if transformation of primary care in Scotland is to become a reality.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".