Body Image Disturbance in Female Adolescents Using Online Learning Platforms: A Concept Analysis
Bibliographic record
Abstract
Aim To analyze the concept of body image disturbance in female adolescents using online learning platforms as a phenomenon of interest to nurse practitioners and other health care practitioners. Background With the declaration of the COVID-19 pandemic, the use of online learning platforms as a primary mode of learning has increased exponentially among adolescents. While research is still lacking in this field, the literature on traditional social media suggests that these online learning platforms may negatively influence body image and emotional outcomes. Particularly vulnerable to these outcomes are female adolescents, whose construction of own body image is highly influenced by their self-perceived evaluation from their peers. While the concept of body image disturbance is well defined in adolescent eating disorders, it has not been characterized in the context of online learning platforms. Defining the concept of body image disturbance in this context is crucial for recognizing its occurrence and providing early intervention. Methods The Walker and Avant method of concept analysis was used to analyze the concept. Findings The defining attributes of body image disturbance among female adolescents using online learning platforms are: (1) Viewing a digitally distorted image of oneself and feeling displeasure with the perceived appearance; (2) Self-surveillance of one’s appearance; and (3) Upward comparison of one’s appearance with others and experiencing feelings of inadequacy. Conclusion As COVID-19 continues to disrupt the traditional school experience, nurse practitioners can use the presented scenarios, along with examples of questionnaires noted in this study, to recognize and delineate the occurrence of body image disturbance in female adolescents using online learning platforms. A standardized definition of the concept will enable nurse practitioners to recognize its occurrence and to provide interventions in a timely manner.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".