Influence of Organizational Issues on Nurse Administrators’ Support to Staff Nurses’ Use of Smartphones for Work Purposes in the Philippines: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Studies show that nurses use their own smartphones for work purposes, and there are several organizational issues related to this. However, it is unclear what these organizational issues are in the Philippines and the influence they have on nurse administrators' (ie, superiors) support to staff nurses' (ie, subordinates) use of smartphones for work purposes. OBJECTIVE: Drawing from the Organizational Support Theory (OST), this study aimed to identify organizational issues that influence nurse administrators' support to staff nurses' use of smartphones for work purposes. METHODS: Between June and July 2017, 9 focus groups with 43 nurse administrators (ie, head nurses, nurse supervisors, and nurse managers) were conducted in 9 tertiary-level general hospitals in Metro Manila, the Philippines. Drawing from OST, issues were classified as those that encouraged or inhibited nurse administrators to support nurses' use of smartphones for work purposes. RESULTS: Nurse administrators were encouraged to support nurses' use of smartphones for work purposes when (1) personal smartphones are superior to workplace technologies, (2) personal smartphones resolve unit phone problems, and (3) policy is unrealistic to implement. Conversely, issues that inhibited nurse administrators to support nurses' use of smartphones for work purposes include (1) smartphone use for nonwork purposes and (2) misinterpretation by patients. CONCLUSIONS: Nurse administrators in the Philippines faced several organizational issues that encouraged or inhibited support to staff nurses' use of smartphones for work purposes. Following OST, the extent of their support can influence staff nurses' perceived organizational support on the use of smartphones for work purposes, Overall, the findings highlight the role and implication of organizational support in the context of smartphone consumerization in hospital settings, especially in developing countries.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".