21 Wellbeing of lone working Healthcare Assistants and its impact on staff retention in hospice care at home services
Bibliographic record
Abstract
Introduction Recent UK-based research revealed the complex role of lone working Healthcare Assistants (HCAs) who provide hospice care at home without direct supervision of a registered practitioner.1 The complexity of the role combined with emotional labour,2 support needs and variable preparation1 may have long term implications on wellbeing and retention. Staff attrition leads to inadequate staffing, unmet care needs, and impacts on patient wellbeing.3 4 Aims To investigate wellbeing, intention to leave and support needs amongst lone working HCAs providing hospice care at home. Methods A mixed-method sequential explanatory design, comprised of two phases 1) a cross-sectional online survey consisting of validated tools and questions designed by the study team, and 2) semi-structured qualitative interviews. Results 218 HCAs completed the survey (12/04/23 to 08/06/23), 14 HCAs completed interviews (19/07/23 to 31/08/23). Mean wellbeing score was 52.2(SD=8.64). Higher wellbeing scores were related to lower intentions to leave r(216) = -.25, p<.001. Qualitative data (open text survey responses and interviews) shown the main factors negatively influencing wellbeing were loneliness, isolation and feeling undervalued. The main factors positively influencing wellbeing were high job satisfaction and finding the role meaningful. HCAs revealed a strong preference for contact-based support (in-person and virtual). In-person contact was an important prerequisite for building further contact with peers (virtual or via phone). However, support available from line manager and peers varied greatly and had a substantial impact on wellbeing levels and intention to leave, both positively and negatively. Conclusion Many factors impact on wellbeing level of lone working HCAs providing hospice care at home. As higher wellbeing level was related to lower intentions to leave, supporting staff wellbeing is a key organisational strategy for higher retention of staff. Impact Understanding HCAs support needs can inform development support strategies to improve wellbeing and increase staff retention. References Patynowska KA, McConnell T, McAtamney C, Hasson F. ‘That just doesn’t feel right at times’ - lone working practices, support and educational needs of newly employed Healthcare Assistants providing 24/7 palliative care in the community: a qualitative interview study. Palliat Med. 2023:2692163231175990. Lovatt M, Nanton V, Roberts J, Ingleton C, Noble B, Pitt E, et al. The provision of emotional labour by health care assistants caring for dying cancer patients in the community: a qualitative study into the experiences of health care assistants and bereaved family carers. Int J Nurs Stud. 2015;52(1):271–9. Almazan JU, Albougami AS, Alamri MS. Exploring nurses’ work-related stress in an acute care hospital in KSA. J Taibah Univ Med Sci. 2019;14(4):376–82. Zaheer S, Ginsburg L, Wong HJ, Thomson K, Bain L, Wulffhart Z. Turnover intention of hospital staff in Ontario, Canada: exploring the role of frontline supervisors, teamwork, and mindful organizing. Hum Resour Health. 2019;17(1):66.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".