Understanding Employee Voice Behavior Through the Use of Digital Voice Channel in Long-Term Care: Protocol for an Embedded Multiple-Case Study
Bibliographic record
Abstract
BACKGROUND: Specific challenges in the health care sector, such as hierarchical structures, shortages of nursing staff, and high turnover of nursing staff, can be addressed by a change process of organizational culture into shared governance. Data from business organizations show that the use of digital voice channels provides employee voice. This approach makes concrete the opportunity for employees to raise their voices by answering surveys and making comments in an anonymous forum, which subsequently positively influences staff turnover and sick leave. Since there is no clear understanding of how a digital voice channel can be used in long-term care to address employee voice, a research gap has been identified. OBJECTIVE: The purpose of ADVICE (Understanding Employee Voice Behavior; the acronym for this study) is to understand how the use of a digital voice channel performs in long-term care (residential long-term care and home care facilities). The aim of this study is to understand how the digital voice channel can support staff in making their voices heard and to see what managers need to use the voice channel to change the work environment. METHODS: An embedded multiple-case study will be used to explore the experiences of 2 health care providers who have already implemented a digital voice channel. ADVICE is organized into two main phases: (1) a scoping review and (2) an embedded multiple-case study. For this purpose, focus group interviews with employees, discursive-dialogical interviews with managers, meeting protocols, and data from the digital voice channel will be analyzed. First, all units of analysis from every embedded unit will be separately analyzed and then comprehensively analyzed to obtain a case vignette from every embedded unit (within-analysis). In the second stage, the analyzed data from the embedded units will be compared with each other in a comparative analysis (cross-analysis). RESULTS: The results will provide insight into how digital voice channels can be used in long-term care to address employee voice. We expect to find how the digital voice channel can empower nurses to speak up and, consequently, create a better work environment. Data collection began in August 2023, and from a current perspective, the first results are expected in summer 2024. CONCLUSIONS: In summary, the results may help to better understand the use of a digital voice channel in the health care sector and its transformative potential for leadership. At the organizational level, research can help to improve the attractiveness of the workplace by understanding how to give employees a voice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48601.
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 | 0.100 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".