Learning from Workforce Studies that Cut Across SocialServices
Bibliographic record
Abstract
Sometimes researchers, administrators and practitioners miss studies that cut across social service fields but that include child welfare workers in their samples. For example, a Canadian study by Graham, Shier, & Nicholas (2016), Workplace Congruence and Occupational Outcomes Among Social Services Workers, has relevant implications for the child welfare field. This study found that when a job setting matches an employee’s expectations regarding workload, autonomy, the working environment, and the values of the organization, there is likely to be a lower intention to leave the job and greater life satisfaction. This finding implies it is important to help new child welfare employees set realistic expectations about the job. The QIC-WD is built on a model of learning from multiple disciplines and workplace settings to help child welfare jurisdictions think about a myriad of ways to improve circumstances for their own workforces. Our team has created Umbrella Summaries which contain information from meta-analyses, systematic and comprehensive reviews on a specific workforce intervention that has been found to impact job retention or performance. Most of these studies were conducted by Industrial-Organizational Psychologists in all types of work settings, but many of the interventions have relevance for child welfare. Building on the example above, the QICWD recently released a summary of the research behind the Realistic Job Previews (RJP), a hiring tool that can be used to address employee expectations. RJPs are designed to provide job candidates with positive and negative information about the job and the organization, for the purpose of influencing employee perceptions, attitudes, job performance, and ultimately, retention. Human Resources professionals and leaders in child welfare jurisdictions can use findings from these studies and meta-analyses to inform their policies and practices.
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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 | 0.135 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.026 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".