Approche appréciative et utilisation des forces en milieu organisationnel : une évaluation comparative de deux démarches associées au bien-être et à la performance au travail
Bibliographic record
Abstract
Strengths and Appreciative Inquiry (AI) interventions can represent an interesting opportunity for organizations wishing to support their employees in the development of their well-being, their commitment and their performance at work. Indeed, in recent years, multiple studies have supported the beneficial effects of such interventions in this regard (Ameer & Zubair, 2020; Conkright, 2011; Harzer et al., 2021; Gradito, Dubord & Forest, 2022; Meyers et al. al., 2019, Oxendine et al., 2022). However, a lack of experimental studies is noted. Furthermore, although these two approaches have common points, a comparison of their effects has never yet been carried out.Thus, the primary objective of this research is to carry out a systematic review of the impacts of the use and development of strengths as well as the use of AI in the workplace. The second objective is to conduct an empirical study, using a quasi-experimental design, allowing, on one hand, to validate the postulated effect of interventions based on AI and on the development of strengths with regard to different aspects relating to the well-being, commitment and performance of employees, and on the other hand, to compare them with each other in a differentiated way.The results demonstrate the beneficial impacts of strengths-based and AI interventions on well-being, performance and engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".