Social Support and Supervisory Quality Interventions in the Workplace: A Stakeholder-Centered Best-Evidence Synthesis of Systematic Reviews on Work Outcomes
Bibliographic record
Abstract
BACKGROUND: There is controversy surrounding the impact of workplace interventions aimed at improving social support and supervisory quality on absenteeism, productivity and financial outcomes. OBJECTIVE: To determine the value of social support interventions for work outcomes. METHODS: Databases were searched for systematic reviews between 2000 and 2012 to complete a synthesis of systematic reviews guided by the PRISMA statement and the IOM guidelines for systematic reviews. Assessment of articles for inclusion and methodological quality was conducted independently by at least two researchers, with differences resolved by consensus. RESULTS: The search resulted in 3363 titles of which 3248 were excluded following title/abstract review, leaving 115 articles that were retrieved and underwent full article review. 10 articles met the set inclusion criteria, with 7 focusing on social support, 2 on supervisory quality and 1 on both. We found moderate and limited evidence, respectively, that social support and supervisory quality interventions positively impact workplace outcomes. CONCLUSION: There is moderate evidence that social support and limited evidence that supervisory quality interventions have a positive effect on work outcomes.
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 | 0.041 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".