Maximising resilience resources for mental healthcare staff
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate optimal resources to promote resilience in staff working in inpatient mental health services. The study also provides an example of card sorting methodology used as an efficient way to identify the most helpful resources for resilience. Design/methodology/approach In total, 25 clinical staff participated in the study. A preliminary focus group and brief literature search identified resources used in two tasks. Two card sorting tasks identified resources participants found helpful vs unhelpful and abundant vs scarce, and resources they would find valuable to use more often. Findings The results indicate that most resources helpful to resilience and available to staff were personal resources (relating to positive outlooks or ways of working), whereas resources valuable to resilience but scarce in the working environment were organisational resources (relating to management or social workplace culture). Resources found to not be valuable to resilience were largely personal tangible resources (e.g. smoking, massages). Practical implications The findings and method may be generalisable to other mental health services, giving insight into promoting resilience within individuals and organisations. This information could serve as guidelines to streamline the allocation of organisational resources to best promote resilience across various mental health settings. Originality/value Staff resilience to working in mental health services contributes to high-quality, sustainable patient care. This study provides further insight into how personal and organisational resources are both vital to resilience in staff working in highly challenging environments.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".