Working and employment conditions of school support staff: a scoping review protocol
Bibliographic record
Abstract
Background: For many years, Quebec’s provincial education system has been facing a serious shortage of school support staff. Recent literature indicates that their working and employment conditions have been identified as barriers to attracting and retaining these staff members. These include job insecurity, fragmented schedules, gender stereotypes and limited power, which affect their recognition within the school system. In addition, the main unions representing school support staff indicate that they are exposed to numerous psychosocial risks (workplace violence, organizational injustice) that can affect their physical and mental health. This creates a vicious circle, as the lack of attractiveness and retention in schools leads to work overload, a deterioration in quality of service (service disruptions, failure to comply with supervision ratios), burnout, and a decline in well-being at work, which in turn fuels the school support staff turnover rate. To date, only a few scientific studies have analyzed the working conditions of school support staff. Most studies rather focus on the quality of educational and pedagogical services offered. To fill this gap, a collaborative research project aims to identify collaborative practices, and contextualize them in terms of working and employment conditions, in order to attract and retain school support staff. Objectives: This scoping review pursues two main objectives: 1) to characterize the working and employment conditions of school support staff and identify their determinants; 2) to identify the impacts of these conditions on their practices and work activity. Methods and analysis: Using qualitative methods, a scoping review will be carried out. The search strategy will be applied to more than fourteen databases (Education source, Medline, Academic search complete, Psychology and behavioural sciences collection, SocINDEX, ERIC, APA Psycinfo, APA PsycArticles, Scopus, Érudit, Persée, Cairn and all databases of SCOPUS). The screening will be done with Covidence, including a training test with two independent researchers to ensure reliability and a reconciliation procedure. An extraction grid will be used to extract data from selected documents. Tables and graphs will summarize the characteristics of the selected studies (year of publication, country, research aim and design, job title, type of school setting, etc.). Research gaps will be highlighted by comparing these characteristics. A semi-inductive thematic coding will be performed to analyze the extracted data. Then, a descriptive synthesis will be written, using a narrative approach, to discuss the main objectives of this scoping review. Finally, recommendations for further research will be formulated.
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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 | 0.117 | 0.092 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".