Information-seeking to Support Personal and Community Wellbeing: Report of a study of New Zealand Men Using Focus Groups
Bibliographic record
Abstract
This paper reports part of an ongoing study exploring the information behaviour of New Zealand men during periods of diminished health and wellbeing. Focus groups were used for this iteration of the study. Results indicate that New Zealand men face both personal and structural constraints to their information-seeking during periods when their health and wellbeing may be compromised. This study highlights that service providers need to develop more effective information delivery mechanisms and support services for men. These services need to be appealing to men and reflect men’s information seeking preferences. The role of LIS professionals in supporting this endeavour is discussed. Cet article présente une étude en cours explorant le comportement informationnel d’ hommes néo-zélandais durant des périodes où leur état de santé et de bien-être est amoindri. Des groupes de discussion ont été utilisés pour cette itération de l'étude. Les résultats indiquent que les hommes en Nouvelle-Zélande font face à des contraintes à la fois personnelles et structurelles dans leur recherche d'information pendant les périodes où leur santé et leur bien-être peuvent être affaiblis. Cette étude met en évidence le besoin pour les fournisseurs de services de développer des mécanismes de diffusion de l'information plus efficaces et des services de soutien pour les hommes. Ces services doivent être attrayants et refléter les préférences des hommes dans leurs recherches d’information. Le rôle des professionnels de l'information dans le soutien à cette entreprise est discuté.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".