Reducing unintended harm: Addressing weight bias as a social justice issue in counseling through justice motive theory.
Bibliographic record
Abstract
Weight bias comprises a multitude of stereotypes about individuals with large bodies, as well as negative attitudes and discriminatory behaviors. In a culture of normative discontent that emphasizes thinness as a social ideal, many counselors will inevitably work with clients who seek assistance with weight loss. Although weight loss may be a client’s identified treatment goal, counselors must consider the potential for harm working toward such a goal in therapy. In this article, we utilize justice motive theory to discuss the potential unintended harm to clients in counseling when weight bias is not considered as part of a case conceptualization. Through the examination of three common social messages related to body weight (i.e., the controllability of body weight, the morality of weight, the social acceptability of weight bias), the justice motive is used to illustrate a case example and the implications for counseling. Considerations for socially just practices in counseling are discussed, as well as future research directions.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".