Relationship Between Academic Library Workers’ Outlooks on Life, Personality, and Goal-Setting Behavior and Achievement
Bibliographic record
Abstract
A Review of: Lo, L.S. & Anderson, A.M. (2020). Personal goal setting behavior and professional outlooks of academic library employees. Journal of New Librarianship, 5, 204-236. https://doi.org/10.33011/newlibs/9/21 Abstract Objective – To identify a correlation between academic library employees who set New Year’s resolutions and goal-setting behavior in professional contexts, and to explore practices, personal attitudes, and outlooks that influence goal-setting and goal-achievement Design – Non-experimental multiple choice questionnaire Setting – Online Subjects – 308 adult participants (over 21 years old) who work in academic library settings including staff, librarians, and administration Methods – The authors designed an online, non-experimental multiple choice questionnaire through Qualtrics. The authors distributed study invitations to multiple professional library listservs, though it is unclear which listservs were included and what geographic location was covered. The survey was available for roughly a month from February 1-26, 2016. The survey screened participant demographics to omit those under 21 years of age and all identifying information was removed in order to protect participant privacy. All participation was voluntary and participants who were interested in contributing to a follow-up research study were asked to share their contact emails. Main Results – Most participants (n=182, 59%) set no New Year's resolutions in 2015 and half (n=155, 50%) set no resolutions in 2016. When asked to explain, 23% noted that they hadn't considered setting resolutions in 2016, 9% did not prioritize setting goals, and 5% felt that they could not achieve their goals. Additionally, over 50% articulated other reasons including not prioritizing goal-setting for New Year’s, noting that setting goals around the academic year was timelier, and that some participants already had enough goals to achieve. In 2016, half of participants (n=153, 50%) set New Year’s resolutions. By far the most common resolution was physical fitness and healthy eating (n=64, 42%). About 19% set occupational goals including skill building, and 15% set emotional goals including cultivating optimism and mindfulness. When asked about goal-setting practices, 36% of the 2016 resolution setters described writing or typing out their goals, 59% shared their goals with others, and nearly 90% enacted changes in their daily routines in order to achieve their goals. 26 participants used all of the goal setting practices above. This group prioritized their top goals and felt confident about reaching those goals. Four participants did not practice goal-setting techniques, and also felt less confident about achieving their goals. 49% of 2016 resolution setters had somewhat optimistic outlooks, and 24% had very optimistic life outlooks. Of those with pessimistic life outlooks, nearly all believed it would be difficult to accomplish goals. Respondents who claimed to be very ambitious were likely to set occupational goals as their top goal. 81% of those in dean and director positions reported being very ambitious and 85% also reported being optimistic. All deans and directors felt confident about accomplishing their goals. For middle managers, 75% felt ambitious and 72% felt optimistic. Professional librarians were 66% ambitious and 72% optimistic. Conclusions – This study's findings align closely with United States national averages about the percentage of Americans who set New Year’s resolutions and achieve their goals. Data suggests some relationship between academic library workers’ outlooks on life and confidence in achieving their goals, as well as a correlation between goal setting strategies and achieving goals. The authors express optimism that 20% of participants who set New Year's resolutions chose to list occupational goals as their top goals, especially considering that resolution-setting comprises an incredibly broad array of options. The authors suggest that data can be used by academic library administrators to increase worker job performance, improve worker wellness, establish mentorship programs, and train workers to set attainable goals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.220 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".