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Record W2808376598 · doi:10.18438/eblip29419

Academic Librarians Perceive Duration and Social Interaction as Important Elements for Professional Development

2018· article· en· W2808376598 on OpenAlexvenueno aff
Hilary Bussell

Bibliographic record

VenueEvidence Based Library and Information Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipProfessional developmentQualitative researchPsychologyPerceptionSet (abstract data type)Duration (music)PedagogySociologyMedical educationSocial psychologyMedicineSocial scienceComputer science

Abstract

fetched live from OpenAlex

A Review of: Attebury, R. I. (2017). Professional development: A qualitative study of high impact characteristics affecting meaningful and transformational learning. The Journal of Academic Librarianship, 43(3), 232-241. http://dx.doi.org//10.1016/j.acalib.2017.02.015 Abstract Objective – To understand the characteristics of meaningful and transformational professional development experiences of academic librarians. Design – Qualitative analysis of in-depth interviews using a hermeneutic phenomenological approach. Setting – Public and private colleges and universities in the United States of America. Subjects – 10 academic librarians. Methods – The researcher selected 10 participants using an initial survey distributed through national library electronic mail lists. Two rounds of semi-structured, in-depth interviews were conducted over Skype during fall 2014 and spring 2015. The first round of interviews began with background questions about participants’ careers, then moved on to questions about professional development experiences that were meaningful and/or transformational. The responses from this first round of interviews were used to develop questions for a second round of interviews with the same participants. After completing the interviews, the researcher sent follow-up emails to participants in order to gather feedback on summaries and interpretations of interviews. The transcribed interviews were used to create an initial set of codes and then imported into NVivo for analysis using a hermeneutic phenomenological approach. Main Results – All participants reported on professional development experiences that they found to be meaningful. Half of the participants discussed professional development experiences that were transformational for their perceptions and practice of librarianship. The themes of duration and interaction were identified in every participant’s discussions of meaningful or transformational professional development. Reflection, discomfort, and self-awareness were also identified as prominent themes. Conclusion – The study found that two of the most important ingredients for meaningful and transformational professional development are activities that are sustained over time and that include social interaction. The participants perceived long-term, interactive professional development activities as opportunities to identify and address gaps in their professional knowledge, which benefits themselves and their organizations. On-the-job learning, single-theme workshops or institutes, and professional committee work were particularly promising forms of meaningful professional development. The author recommends that academic librarians who are interested in meaningful or transformational professional development look for activities that are sustained and interactive, that promote reflection, and that provide opportunities to increase self-awareness of gaps in knowledge. Facilitators of professional development activities should include interactive components and ensure that participants have a chance to stay in contact after the event in order to encourage long-term interaction and reflection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0070.004
Scholarly communication0.0110.007
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.027
GPT teacher head0.346
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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