Interventions to promote or improve the mental health of primary care nurses: a systematic review
Bibliographic record
Abstract
AIM: To synthesize the evidence on the effectiveness of interventions aiming to promote or improve the mental health of primary care nurses. BACKGROUND: Primary care nurses have been found to have high levels of emotional exhaustion and to be at increased risk of suffering from burnout, anxiety and depression. Given the increasingly critical role of nurses in high-performing primary care, there is a need to identify interventions that can effectively reduce these professionals' mental health problems and promote their well-being. EVALUATION: We conducted a systematic review on the effectiveness of interventions at the individual, group, work environment or organizational level. KEY ISSUES: Eight articles reporting on seven unique studies met all eligibility criteria. They were non-randomized pre-post intervention studies and reported positive impacts of interventions on at least some outcomes, though caution is warranted in interpreting these results given the moderate-weak methodological quality of studies. CONCLUSIONS: This systematic review found moderate-weak evidence that primary, secondary and combined interventions can reduce burnout and stress in nurses practising in community-based health care settings. IMPLICATIONS FOR NURSING MANAGEMENT: The results highlight a need for the implementation and evaluation of new strategies tailored for community-based nurses practising in primary care.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".