The Career Experiences of Noninstructional Itinerant Staff in K–12 Public Schools with Demonstrated Longevity
Bibliographic record
Abstract
Shortages in noninstructional itinerant staff (school nurses, school psychologists, school social workers) have been especially difficult for K–12 public school districts, as these individuals have critical responsibilities within the school setting that they are uniquely qualified to complete. Noninstructional itinerant employees face challenges such as isolation, role confusion, and high workloads that professionals who work in other settings or instructional colleagues may not encounter. They may also be impacted by their responsibilities related to meeting the increasing mental health needs of the students they support. The aim of this qualitative research study was to consider the experiences of 14 noninstructional itinerant professionals - five school nurses, five school psychologists, and four school social workers - who have been employed in the same K-12 public school setting for at least 10 years. Several themes emerged as the findings of the study: passion/purpose, expertise, working conditions, connections, and personal characteristics. A majority of the noninstitutional itinerants in this study reported finding deep purpose in their work and having a long-term impact on the lives of students. For most professionals, this factor may mitigate adverse working conditions such as lower pay and higher caseloads. In the area of working conditions, the school schedule emerged as a leading motivator, as it provided for a better work-life balance. Another dominant factor was connections with administration, which influenced the level of inclusion, provision of adequate workspace, and professional input. The personal factors that impacted longevity were the noninstructional itinerants’ level of flexibility and resiliency.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".