Developing and testing scales for home support service continuity (HSSC): cross-sectional studies in Canada and UK
Bibliographic record
Abstract
Objectives Ensuring the continuity of home support services has become increasingly important due to challenges arising from ageing demographics and healthcare staffing shortages. However, there is a lack of validated measurements specifically designed for assessing service continuity in this context. The primary objective of this study is to develop and validate scales that capture the multidimensional nature of home support service continuity (HSSC), incorporating informational continuity, management continuity and relational continuity as its underlying components. Subsequently, these scales are employed to measure the overall level of continuity experienced within home support services and investigate its association with service quality. Methods This study used a cross-sectional survey design with convenience sampling. Direct caregivers in the UK were recruited through the Prolific UK online platform, while direct caregivers in British Columbia, Canada were recruited through local health authorities and home support agencies. A total of 550 direct caregivers completed the online survey following the approved ethics protocol. Structural equation modelling was employed to evaluate HSSC and it underlying components. Furthermore, the study investigated the influence of HSSC on service quality within these two samples. Results The quantitative tests confirmed that HSSC comprises three first-order continuity components. These components showed significant loadings on HSSC in the Canadian sample (N=367) (λ informational =0.81, λ management =0.93, λ relational =0.38) at p<0.01 level. This finding was further supported in the UK sample (N=183) (λ informational =0.87, λ management =0.90, λ relational =0.93) at p<0.01 level. In both samples, the overall HSSC showed a positive correlation with service quality (path coefficient for the Canadian sample: b HSSC_employee perceived service quality (EPSQ) =0.22, p<0.01; the UK sample: b HSSC_EPSQ =0.70, p<0.01). Conclusions The results support the conceptualisation of HSSC as a second-order latent construct. The newly developed and validated scales for the three first-order constructs identify specific items that could be targeted to improve HSSC and service quality.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".