Improving The Effectiveness Of Human Resources Practices Through Transforming Care At The Bedside
Bibliographic record
Abstract
In 2010, in an effort to increase patient involvement in decision-making about health care redesign, a Quebec university health care organization implemented the Transforming Care at the Bedside (TCAB). This article presents the results from a qualitative study exploring health professionals’ perceptions of TCAB and the effect on turnover and overtime. This descriptive, qualitative study utilized focus groups, individual interviews, and a review of administrative documents for data collection. Participants included hospital workers from five units implementing TCAB. The data generated by the interviews and focus groups were analyzed using NVivo with the method proposed by Miles and Huberman (1994). During the first year of implementation of TCAB, the team noted the importance of taking time to see the effects of the changes and thereby facilitate the involvement of other team members. A number of TCAB team members also cited communication as a facilitating element for informing team members of changes. According to the participants, the TCAB strategies that were implemented have had a positive impact on practice and on the work environment, and turnover showed an improvement. There was no change in absenteeism. TCAB has the potential to impact not only nurses’ work, but interprofessional team work as well, through changes that involve everyone. Future research should focus on how to support team members to reduce resistance to change and increase social support in order to implement and sustain changes.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".