The Relationship Between Social Media and Body Image in Adult Women: Implications for Counselling Practice
Bibliographic record
Abstract
Social media is a significant component in the lives of people across generations. In this paper, social media refers to online applications (APPs) or websites that allow users to share personal content, seek information, and engage with one another. Some social media platforms include Instagram, Facebook, Twitter, TikTok, YouTube, and Snapchat. In the past, traditional media and social media platforms showcased individuals meeting thin body ideals (Jiotsa et al., 2021). Social media platforms have expanded to include a wide range of body types and shifted towards inclusivity and a culture of body positivity (Hynnä & Kyrölä., 2019). With many adults accessing social media, it is prudent to understand how it may impact the way older individuals view themselves and their behaviour. Reports show that 97.9% of Canadian individuals aged 15–24 own a smartphone, with over half using their phone every 30 minutes, and these numbers are stable as people age (Statistics Canada, 2018). Research surrounding the effects of social media can help uncover who is prone to the possible negative implications of social media use and what steps to take toward education and prevention. The main objective of this research is to uncover the relationship between social media and the body image of adult women and what action mental health professionals can take to minimize the negative consequences of media use. I hope to address the following research questions: How does social media influence adult women’s body image and perception of self, and what interventions can be implemented to support and educate adults using social media to prevent negative consequences?
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".