Enlivening a Community of Authentic Scholarship
Bibliographic record
Abstract
Background: Critical and engaged qualitative scholarship depends on high-quality graduate training. The need to reexamine graduate student mentorship has become particularly pressing, given the high level of mental health distress experienced by students. It is unclear whether mentorship emerging within the student–advisor relationship is sufficient to ensure comprehensive mentorship. Innovative, experiential pedagogical approaches that integrate emotional and intellectual aspects are limited but may play a vital role in mentorship. There is a critical need to develop and study creative mentorship initiatives for emerging qualitative scholars. Methods: This study used interpretive description methodology and a community of practice theoretical framework to describe a faculty-mentored experience for graduate nursing students at the 2016 Qualitative Health Research Conference (FM-QHR) hosted by the International Institute for Qualitative Methodology. Participants completed written journals elucidating their experiences throughout FM-QHR. The textual data were analyzed using a constant comparative group analysis process, leading to the development of salient and interconnected themes. Results: Six graduate students and four faculty mentors submitted journals. Three interrelated themes articulate how this FM-QHR initiative enlivened a community of authentic scholarship: Questioning the Academic Self: Unvoiced Experiences of Angst, Uncertainty, and Fear; Cocreating Authentic Community through Shared Vulnerability; and Generative and Emergent Empowerment. Conclusion: These findings provide compelling insights into the importance of assisting students to navigate the emotional experiences that are a part of qualitative graduate training. Relational, mentorship initiatives hold potential to not only alleviate emotional distress but also support student empowerment, socialization, and entrance into a community of international qualitative researchers.
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.
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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".