Psychological assessment in school contexts: ethical issues and practical guidelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Psychological assessment in school settings involves a range of complexities and ethical dilemmas that practitioners must navigate carefully. This paper provides a comprehensive review of common issues faced by school psychologists during assessments, discussing best practices and ethical guidelines based on codes from various professional organizations. METHODS: We examine the entire assessment process, from pre-assessment considerations like informed consent and instrument selection to post-assessment practices involving results communication and confidentiality. Key ethical concerns addressed include fairness in assessment, cultural and linguistic appropriateness of testing materials, and issues surrounding informed consent. RESULTS: Specific challenges discussed include selecting appropriate assessment instruments that reflect the diverse needs and backgrounds of students, ensuring fairness and removing bias in testing, and effectively communicating results to various stakeholders while maintaining confidentiality. We emphasize the importance of multi-source, multi-method assessment approaches and the critical role of ongoing professional development in ethical practice. CONCLUSION: By adhering to established ethical standards and best practices, school psychologists can effectively support the educational and developmental needs of students. This paper outlines actionable recommendations and ethical considerations to help practitioners enhance the accuracy, fairness, and impact of their assessments in educational settings.
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it