Too Anxious to Talk: Social Anxiety, Communication, and Academic Experiences in Higher Education
Bibliographic record
Abstract
The first overarching goal of this doctoral dissertation was to develop and measure a new construct termed academic communication.Accordingly, Study 1 focussed on item development, pilot testing, and examining the psychometric properties of the newly developed Academic Communication Inventory (ACI).Undergraduate students (N = 642, Mage = 19.5) completed the ACI (assessing general communicative behaviours) along with other measures to investigate external validity.Results demonstrated that the best fitting structure of the ACI was a two-factor solution, consisting of the subscales: (1) communication with instructors; and (2) communication with peers.Study 2 assessed measure invariance across educational context (i.e., blended courses, online courses, offline courses), as well as gender differences in communication.Participants were undergraduate students (N = 1074, Mage = 20.3)who completed the ACI (assessing course-specific communicative behaviours), with 21% subset completing follow-up questionnaires (participants from Study 2 were also used in Studies 3 and 4 for different research purposes).Multi-group factor analyses suggested that the ACI could be used as both a general and course-specific measure of academic communication (i.e., the ACI was invariant across course contexts).Moreover, females and males reported different communication levels with instructors and peers.Study 3 focused on the utility of the ACI, by examining the links between social anxiety, communication, academic experiences (i.e., engagement, classroom connectedness, student satisfaction) and wellbeing.Among the results, academic communication accounted for significant variance in the links between social anxiety and academic experiences.Moreover, social anxiety was negatively related to academic experiences, and there was at least some conversations about academia, research, and life in general.Rob, you never failed to provide advice that helped guide me through the throes of graduate school.Your wisdom, patience, encouragement, and unconditional support made this all possible.I am so lucky to call you my mentor and friend.I would like to thank the Coplan lab (past and present), for being there for brainstorming sessions, to vent frustrations, and for all the games nights.You helped to create a safe and fun space during a time of immense stress and pressure.A special shout out to Laura -I won't ever forget the hundreds of hours spent on the phone (you're welcome, Rogers), the emotional support you provided, your problem-solving abilities, and conferences.To my family, last but definitely not least.Thank you for always having faith in my abilities, and for always boosting my spirits when I needed it.Mom and Dad, throughout my life, you provided me with the tools I needed to excel.I would not be where I am without your unwavering support and encouragement.To my husband, Jason -thank you for encouraging me to move to Ottawa so many years ago, so I could pursue my MA and PhD with Rob.Even though we were not always in the same city, you were always there to support my aspirations, provide reassurance, and make me laugh.I am very fortunate to have a partner like you to experience life with.Finally, to my little Brielle -your presence gave me the final push I needed to finish my dissertation.And for that, along with all the joy and love you bring into my life, I will forever be grateful.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".